Bert Kappen

Bert (HJ) Kappen





The physics of inference and control

Keywords: Bayesian inference, learning and reasoning, stochastic control theory, neural networks, statistical physics

One of the marked differences between computers and animals is the ability of the latter to learn and flexibly adapt to changing situations. Whereas computers need to be programmed with provisions for all possible future circumstances, the brain adapts its 'program' when needed, striking a remarkable balance between flexibility to adapt on the one hand and persistence by re-using pre-learned facts and skills on the other. This is commonly referred to as intelligent behavior. Particular examples of intelligence are pattern recognition, learning and memory, reasoning, planning and motor control.

The question how intelligence arises and how it is computed in the animal brain is not well understood. One can try to reproduce intelligence in artificial systems and the problems of how intelligence is encoded in the brain and how it can be created artificially in computers are clearly related. In addition to being an important intellectual challenge in itself, artificial intelligence research also has clear practical implications. We currently witness an explosion of applications in machine learning - the formal study of how machines learn - in for instance robotics and data analysis.

Due to the essential roles that noise and uncertainty play in perception and learning, a useful way to model intelligence is to use probability models. In the mid 90s, the fields of analog and digital computing as separate approaches to model intelligence, have begun to merge using the idea of Bayesian inference: One can generalize the logic of digital computation to a probabilistic calculus, embodied in a so-called graphical model. Similarly, one can generalize dynamical systems to stochastic dynamical systems that allow for a probabilistic description in terms of a Markov process. The Bayesian paradigm has greatly helped to integrate different schools of thought in particular in the field of artificial intelligence and machine learning but also provides a computational paradigm for neuroscience.

My research is dedicated to the design of efficient and novel computational methods for Bayesian inference and stochastic control theory using ideas and methods from statistical physics. These novel methods are used by me and by others in artificial intelligence research and computational models of brain function.

Bayesian Inference

Bayesian models are probability models and the typical computation, whether in the context of a complex data analysis problem or in a stochastic neural network, is to compute an expectation value, which is referred to as Bayesian inference. Bayesian inference is intractable, which means that computation time and memory use scale exponentially with the problem size. However, many methods exist to compute these quantities approximately. Most of these methods origin from statistical physics, such as the mean field method, belief propagation or Monte Carlo sampling. Application of these methods to machine learning problems is challenging and an active field of research to which I have made several contributions. Current projects focus on the application of these ideas in concrete problems:

Control theory

Control theory is a theory from engineering that gives a formal description of how a system, such as a robot or animal, can move from a current state to a future state at minimal cost, where cost can mean time spent, or energy spent or any other quantity. Control theory is used traditionally to control industrial plants, airplanes or missiles, but is also the natural framework to model intelligent behavior in animals or robots. The mathematical formulation of deterministic control theory is very similar to classical mechanics. In fact, classical mechanics can be viewed as a special case of control theory. Stochastic control theory uses the language of stochastic differential equations. For a certain class of stochastic 1 control problems, the solution is described by a linear partial differential equation that can be solved formally as a path integral. This so-called path integral control method provides a deep link between control, inference and statistical physics. This statistical physics view of control theory shows that qualitative different control solutions exist for different noise levels separated by phase transitions. The control solutions can be computed using efficient approximate inference methods such as Monte Carlo sampling or deterministic approximation methods. The path integral control theory is successfully being used by leading research groups in robotics world wide. Some current research directions: The path integral control theory provides an elegant theoretical framework to integrate Bayesian learning from sensory data and motor control, ie. sensorimotor integra- tion. The sensing and motor problems are intricately interrelated: The sensory problem is not to learn 'everything' but only those aspects that are relevant for 'survival' and for the effective computation of the control task(s); The motor problem is in part to explore the world in such a way that relevant sensory information is gathered for the effective execution of future motor tasks. We are currently exploring this problem in collaboration with partners in neuroscience and robotics.

Computational neuroscience

Another line of research, that was started in 2000 and is still continued, is on the effect that short-term synaptic plasticity has on memory storage. The common understanding of long term memory is that it is stored in the synaptic connections between neurons in such a way that memory retrieval occurs as the relaxation of the neural activity to a constant spiking pattern, that represents the memory. This idea was put forward by Hopfield (1982) and others as the attractor neural network. Synaptic dynamics challenges this mechanism, since persistent pre-synaptic activity typically weakens the synaptic strength. The inclusion of short-term synaptic plasticity in an attractor neural network make memories metastable states that rapidly switch from one state to the next, depending on the sensory context. This work provides some insights on the puzzle how the brain, viewed as a dynamical system, is able to build stable representations of the world and at the same time is capable to effortlessly switch between them. With (J. Torres, University of Granada).

Data analysis

Modern experimental neuro-science has been revolutionarized by sophisticated measurement equipment, such as fMRI, MEG and others. Also, the advances in EEG measurement systems has accelerated the research on Brain Computer Interfaces. Thirdly, research tools in genetics have led to an explosion of DNA and expression data. These massive data sets require advanced data analysis tools. Machine learning methods (kernel methods, sparse dimension reduction methods, ICA, Bayesian approaches) provide the most promising approach to analyze these data.

Brain Computer Interface

Since 2009, I have started research on the design of an adaptive BCI system, based on the idea that subjects will be surprised when the BCI output differs from their expectation. This surprise is measurable as a so-called error potential. The detection of the error potential can be used to adapt the BCI device, using the perceptron learning rule (V. Gomez and A. Llera with O. Jensen, Donders Neuro-imaging Center).

Applications

Bayesian methods have a big potential for immediate application in areas outside science. There is a long-standing and quite unique tradition in the SNN group to build such application together with her spin-off companies Smart Research and Promedas. Here are a few examples:

Genetic inference

We have applied an advanced approximate inference method (the Cluster Variation Method) to construct haplotypes in complex pedigrees. The method was shown to outperform the state-of-the-art Monte Carlo sampling approach on a subset of problems. The software is publicly available. Contact Kees Albers for details caa at sanger dot ac dot uk.
Aladin is a software tool for performing efficient linkage analysis of a small number of distantly-related individuals. It estimates multipoint IBD probabilities and parametric LOD scores. Contact Kees Albers for details caa at sanger dot ac dot uk.

Oil exploration

For Shell, we built a petrophysical expert system. It estimates the type of soil and the probability that it contains oil, gas or other valuable minerals, based on drilling measurements. The system is based on a Bayesian network where the probability computation is done using a Monte Carlo sampling method. See Smart Research for further details and other products.

Victim identification

For the Netherlands Forensic Institute, we are building a victim identification system by matching of their DNA profiles against the Pedigrees of Relatives from Missing Person's DNA profiles in large databases, using a Bayesian network. See Bonaparte for further details.

Promedas

We have built the world largest and most up-to-date medical expert system for diagnostic advice in internal medicine. The system is being commercialized by Promedas bv. The system is since end 2008 operational at the Utrecht academic hospital. See Promedas for further details.

Wine and food

We have built a system that selects the most appropriate wines to combine with your food Wine wine wine.

Teaching

Inleiding Machine Learning BA
Computational Neuroscience MA
Machine Learning MA
Introduction to Pattern Recognition MA
Computational Physics MA
Information, Physics, and Computation MA

Short course on control theory, ACNS
Short course on machine learning, Pompeu Fabra spring 2003
Short course on control theory, Madrid fall 2010
Short course on control theory, UCL 2011
Short course on control theory, Madrid 2012
Tutorial ICTP Summer school Machine Learning, Trieste 2012
Short course on control theory, Madrid 2013
Tutorial ICTP Spring College on Physics of Complex systems, Trieste May-June 2013
Short course on control theory, Madrid 2014

Introduction to Biophysics BA (discontinued)
Neural networks and Information theory BA (discontinued, content moved to Inleiding Machine Learning BA from 2012-2013)
Neurophysics BA (discontinued, content moved to Computational Neuroscience MA from 2013-2014)
Neurophysics MA (discontinued)

Contact information

SNN Machine Learning
Radboud University
Huygensgebouw 00.829
Heyendaalseweg 135
NL 6525 AJ Nijmegen
The Netherlands
+31 24 3614241 (phone)
b.kappen-at-science-dot-ru-dot-nl

Recent meetings organized

NIPS Workshop Probabilistic optimal control, Whistler 2009
SNN Symposium Intelligent Machines, Nijmegen 2010
School on large scale problems in machine learning and workshop on common concepts in machine learning and statistical physics, ICTP Trieste 2012
Workshop on the statistical physics of inference and control theory, Granada 2012 website and videolectures
LSOLDM 2013
NIPS 2013 Workshop Planning with Information Constraints for control, reinforcement learning, computational neuroscience, robotics and games
LSOLDM 2014

Organizations and collaborations

Foundation for Neural Networks (SNN)
Ruedi Stoop (ETH) and the European Center for Recreational Physics
Granada computational neuroscience and neurophysics group
Pattern Analysis, Statistical Modelling and Computational Learning (Pascal 2)
Visiting professor at Gatsby Computational Neuroscience Unit, University College London

Current group members

Wim Wiegerinck is senior researcher and is working on approximate inference, genetic inference and various applications and is associate director of Smart Research bv
Vicenç Gómez is postdoc. He is working on ECoG data analysis, approximate inference, neural networks and internet communities and CompLACS (EU FP7) project.
Kevin Sharp is a postdoc on a project to develop Bayesian Gaussian process methodes for GWAS
Joris Bierkens is postdoc on the topic of stochastic optimal control in the CompLACS (EU FP7) project.
Takamitsu Matsubara is assistant professor of robotics at the Nara Institue of Science and Technology on sabatical leave in our group for 2013
Alberto Llera is PhD student on the topic of Brain Computer Interfaces
Sep Thijssen is a PhD student on the application of stochastic optimal control methods for multi-agent systems
Hans Ruiz is a PhD student on the NETT project. He works on the application of stochastic optimal control methods in neuroscience for multi-agent systems
Dominik Thalmeier is a PhD student on the NETT project. He works on the application of stochastic optimal control methods in neuroscience for multi-agent systems
Willem Burgers is programmer for Smart Research bv

Former group members

Tom Heskes (Radboud University Nijmegen) was PhD student and postdoc on online learning.
Martijn Leisink (D66) was PhD student and postdoc on approximate inference
Taylan Cemgil (Bogazici University, Turkey) was PhD student on time-series modeling of music
Joris Mooij was PhD student on approximate inference.
Kees Albers was PhD student and postdoc on approximate inference methods for genetic linkage analysis. Kees was 4 years at Sanger Institute, Cambridge UK and is since 2012 at Human Genetics in Nijmegen.
Bram Kasteel was Bachelor student on the topic of multi-agent control
Stijn Tonk was Master student on the topic of multi-agent control
Ender Akay was programmer for Smart Research bv and Promedas bv
Gulliver de Boer was Bachelor student on the topic of multi-agent control applied to poker
Max Bakker was a Bachelor student on the topic of multi-agent systems
Ben Ruijl was a Bachelor student on the topic of multi-agent systems
Henk Griffioen was Master student on the topic of genetic association studies
Bart van den broek was PhD student on the topic of stochastic optimal control theory
Mohammad Azar was PhD student on the topic of reinforcement learning, now at Carnegie Mellon University
Patrick Lessmann was PhD student on the topic of stochastic optimal control in the CompLACS (EU FP7) project.
Elena Zavgorodnyaya was Master student on the topic of Brain Computer Interfaces.
Martin Mittag was a master student on the topic of stochastic optimal control theory with neural networks
Dick van den Broek was a master physics student on the topic of multi-agent systems
Bram Kasteel was a master physics student on the topic of stochastic optimal control theory
Christiaan Schoenaker was a master physics student on the topic of Super Modeling by combining imperfect models (SUMO)
Jonas Ahrendt was a Artificial Intelligence student on the topic of genetic pedigrees and Bonaparte
Joris Bukala was a bachelor physics student on the topic of Monte Carlo methods
Gulliver de Boer was a master physics student on the topic of genetic linkage analysis with Gaussian Process Regression and implementation on GPUs

Externally funded projects

BrainGain (Smart mix)
Genetic association study using machine learning methods (Donders internal round)
Brain imaging, genetics and psychiatry using machine learning methods (NWO Cognition)
Multi-agent systems (with Thales D-Cis lab)
Bonaparte (Smart Research with the Dutch Forensic Institute)
Bovinose (Smart Research, EU FP7)
CompLACS (EU FP7)
SUMO (EU FP7)
NETT

Vacancies

There are currently no vacancies ckv.docx

Publications

2014
Chernyak V., Chertkov, M, Bierkens J., Kappen H.J. file type imageStochastic optimal control as non-equilibrium statistical mechanics: calculus of variations over density and current Journal of Physics A: Mathematical and Theoretical as a Fast Track Communication, vol. 47, no. 2, pp. 022001, 2014 bibtex

file type imageAdaptive multi class classification on bci Neural Computation, pp. 1-20, 2014 bibtex

file type imageLatent kullback leibler control for continuous-state systems using probabilistic graphical models Proceedings UAI, vol. 30, pp. 1-12, 2014 bibtex

Policy search for path integral control LNAI conference proceedings, pp. 1-16, 2014 bibtex

Bierkens J., Kappen H.J. file type imageExplicit solution of relative entropy weighted control Systems and Control Letters, pp. 1-16, 2014 bibtex

2013
file type imageThe variational garrote Machine Learning Journal, pp. 1-26, 2013 bibtex

Gheshlaghi Azar M., Munos R., Ghavamzadaeh M., Kappen H.J. Speedy q-learning: a computationally efficient reinforcement learning algorithm with a near optimal rate of convergence Journal of Machine Learning Research, 2013 bibtex

Gheshlaghi Azar M., Munos R., Kappen H.J. file type imageMinimax pac bounds on the sample complexity of reinforcement learning with a generative model Machine Learning Journal, vol. 91, no. 3, pp. 325-349, 2013 bibtex

Bierkens J., Chertkov, M, Kappen H.J. file type imageLinear pdes and eigenvalue problems corresponding to ergodic stochastic optimization problems on compact manifolds pp. 1-16, 2013 bibtex

Torres J.J., Kappen H.J. file type imageEmerging phenomena in neural networks with dynamic synapses and their computational implications Frontiers in Computational Neuroscience, vol. 7, no. 30, pp. 1-13, 2013 bibtex

Wiegerinck W.A.J.J., Burgers W.G., Kappen H.J. file type imageBayesian networks, introduction and practical applications Handbook on Neural Information Processing, vol. 49, pp. 401-431, 2013 bibtex

file type imageStochastic optimal control and sensori-motor integration Technical Report, pp. invited paper, 2013 bibtex

Kappen H.J. Comment: causal entropic forces Technical Report, pp. http://arxiv.org/abs/1312.4185, 2013 bibtex

file type imageClustered common spatial patterns TOBI Workshop IV, pp. 117-119, 2013 bibtex

Is task selection a solution for bci illiteracy? Journal of Neural Engeneering, 2013 bibtex

Thijssen S.A., Kappen H.J. Stochastic path integral control International Journal of Control, 2013 bibtex

2012
file type imageOptimal control as a graphical model inference problem Machine Learning, vol. 87, no. 2, pp. 159-182, 2012 bibtex

file type imageA likelihood-based framework for the analysis of discussion threads World Wide Web, pp. 1-31, 2012 bibtex

Adaptive classification on brain computer interfaces using reinforcement signals Neural Computation, vol. 24, no. 11, pp. 2900-2923, 2012 bibtex

Gheshlaghi Azar M., Munos R., Kappen H.J. file type imageOn the sample complexity of reinforcement learning with a generative model Proceedings of the International Conference on Machine Learning Learning, vol. 29 th, pp. 1-11, 2012 bibtex

Torres J.J., Kappen H.J. file type imageEmerging phenomena in neural networks with dynamic synapses and their computational implications Frontiers in Neuroscience, 2012 bibtex

Mejias FF., Kappen H.J., Longtin A., Torres J.J. file type imageShort-term synaptic plasticity and heterogeneity in neural systems Granada Seminar AIP Proceedings 2013, 2012 bibtex

file type imageDynamic policy programming Journal of Machine Learning Research, no. 13, pp. 3207-3245, 2012 bibtex

file type imageLearning price-elasticity of smart consumers in power distribution systems. SmartGrid Comm 2012, Symposium ion Architectures and Models for the SmartGrid, pp. invited paper, 2012 bibtex

Tramper J.J., Broek J.L. van den, Kappen H.J., Gielen C.C.A.M. Time-integrated position error accounts for sensorimotor behavior in time-constr ained tasks. Plos One, vol. 7, no. 3, pp. e33724, 2012 bibtex

2011
file type imageOn the use of interaction error potentials for adaptive brain computer interfaces Neural Networks, vol. 24, pp. 1120-1127, 2011 bibtex

Kappen H.J. file type imageOptimal control theory and the linear bellman equation Inference and Learning in Dynamic Models, pp. 363-387, 2011 bibtex

Tramper J.J., Broek J.L. van den, Wiegerinck W.A.J.J., Kappen H.J., Gielen C.C.A.M. Stochastic optimal control predicts human motor behavior in time-constrained sensorimotor tasks Biological Cybernetics, pp. xx, 2011 bibtex

Gheshlaghi Azar M., Munos R., Ghavamzadaeh M., Kappen H.J. file type imageSpeedy q-learning NIPS 2011, Advances in Neural Information Processing Systems 24, vol. 25, pp. 2411--2419, 2011 bibtex

Bierkens J., Kappen H.J. file type imageOnline solution of the average cost kullback-leibler optimization problem NIPS 2011, 4th International Workshop on Optimization for Machine Learning, vol. 25, pp. 1-6, 2011 bibtex

file type imageModeling the structure and evolution of discussion cascades. Proceedings of the 22nd ACM Conference on Hypertext and Hypermedia, vol. 22, pp. 181-190, 2011 bibtex

Broek J.L. van den, Wiegerinck W.A.J.J., Kappen H.J. Stochastic optimal control of state constrained systems International Journal of Control, vol. 84, no. 3, pp. 597-615, 2011 bibtex

file type imageDynamic policy programming with function approximation JMLR: Workshop and Conference Proceedings: AISTATS 2011, vol. 15, pp. 119-127, 2011 bibtex

Kappen H.J. file type imageMachines - now with intelligence Donders Institute for Brain, Cognition and Behaviour, no. 17, pp. 4-5, 2011 bibtex

2010
Wiegerinck W.A.J.J., Kappen H.J., Burgers W.G. file type imageBayesian networks for expert systems, theory and practical applications Interactive Collaborative Information Systems, vol. SCI 281, pp. 547-578, 2010 bibtex

Burgers W.G., Wiegerinck W.A.J.J., Kappen H.J., Spalburg M. file type imageA bayesian petrophysical decision support system for estimation of reservoir compositions Expert Systems With Applications, vol. 37, no. 12, pp. 7526-7532, 2010 bibtex

file type imageApproximate inference on planar graphs using loop calculus and belief propagation Journal for Machine Learning Research (JMLR), vol. 11, pp. 1273-1296, 2010 bibtex

Mensink T., Verbeek J., Kappen H.J. file type imageEp for efficient stochastic control with obstacles ECAI, pp. 1-6, 2010 bibtex

Kappen H.J., Tonk S. file type imageOptimal exploration as a symmetry breaking phenomenon no. TR1001, pp. 1-5, 2010 bibtex

Broek J.L. van den, Wiegerinck W.A.J.J., Kappen H.J. file type imageRisk sensitive path integral control UAI, vol. 26, pp. 1-8, 2010 bibtex

Mejias FF., Kappen H.J., Torres J.J. file type imageIrregular dynamics in up and down cortical states Plos One, vol. 5, no. 11, pp. 1-13, 2010 bibtex

2009
file type imageApproximate inference on planar graphs using loop calculus and belief propagation Proceedings UAI, vol. 25, pp. no pages, 2009 bibtex

file type imageSelf-organization using synaptic plasticity Advances in Neural Information Processing Systems, vol. 22, pp. 513-520, 2009 bibtex

Mooij J.M., Kappen H.J. file type imageBounds on marginal probability distributions Neural Information Processing Systems, vol. 22, pp. 1105-1113, 2009 bibtex

Mooij J.M., Kappen H.J. file type imageNovel bounds on marginal probabilities Journal for Machine Learning Research (JMLR), 2009 bibtex

file type imageSparse matrix factorization for brain computer interfaces_ Proceedings of ABCI workshop, vol. xx, no. xx, pp. xxx, 2009 bibtex

Mejias FF., Torres J.J., Johnson S., Kappen H.J. file type imageSwitching dynamics of neural systems in the presence of multiplicative colored noise Bio-Inspired Systems: Computational and Ambient Intelligence, pp. 17-23, 2009 bibtex

Janss L., Kappen H.J. file type imageBayesian construction of perceptrons to predict phenotypes from 584k snp data. PASCAL Computational Statistics Workshop, pp. Presentation, 2009 bibtex

Gheshlaghi Azar M., Kappen H.J. Dynamic policy programming with kl-divergence minimization NIPS Workshop on Probabilistic Approaches for Stochastic Optimal Control and Robotics, 2009 bibtex

2008
Broek J.L. van den, Wiegerinck W.A.J.J., Kappen H.J. file type imageGraphical model inference in optimal control of stochastic multi-agent systems Journal of Artificial Intelligence Research, vol. 32, pp. 95-122, 2008 bibtex

Broek J.L. van den, Wiegerinck W.A.J.J., Kappen H.J. file type imageOptimal control in large stochastic multi-agent systems Adaptive Agents and Multi-Agent Systems III. Adaptation and Multi-Agent Learning, vol. 4865, pp. 15-26, 2008 bibtex

Broek J.L. van den, Wiegerinck W.A.J.J., Kappen H.J. file type imageOptimal control in large stochastic multi-agent systems Alamas 07, Maastricht 2-3 April., pp. 9-20, 2008 bibtex

Albers C.A., Stankovich J., Thomson T., Bahlo M., Kappen H.J. Multipoint approximations of identity-by-descent probabilities for accurate linkage analysis of distantly-related individuals American Journal of Human Genetics, vol. 82, no. web only, pp. 607-622, 2008 bibtex

Welling M., Teh Y., Kappen H.J. file type imageHybrid variational / gibbs collapsed inference in topic models UAI, vol. Website Only, pp. 1-8, 2008 bibtex

2007
Albers C.A., Kappen H.J. Modeling linkage disequilibrium in exact linkage BMC Proceedings of the genetic Analysis Workshop 15: Gene Expression Analysis and Approaches to Detecting Multiple Functional, vol. 1, pp. S159, 2007 bibtex

file type imageTruncating the loop series expansion for bp. Journal for Machine Learning Research (JMLR), vol. 8, pp. 1987-2016, 2007 bibtex

Wiegerinck W.A.J.J., Broek J.L. van den, Kappen H.J. file type imageOptimal on-line scheduling in stochastic multi-agent systems in continuous space and time AAMAS'07, vol. website, pp. 1-8, 2007 bibtex

Kappen H.J. file type imageAn introduction to stochastic control theory, path integrals and reinforcement learning. In 9th Granada seminar on Computational Physics: Computational and Mathematical Modeling of Cooperative Behavior in Neural Systems., pp. 149-181, 2007 bibtex

Torres J.J., Marro J., Cortes J.M., Kappen H.J. Attractor neural networks with activity-dependent synapses: the role of synaptic facilitation. Neurocomputing, vol. 70, no. 10-12, pp. 2022-2025, 2007 bibtex

Rizzo T., Wemmenhove B., Kappen H.J. file type imageCavity approximation for graphical models Physical Review E, section Statistical physics, vol. Rev. E 76, no. 011102, pp. 9 pages, 2007 bibtex

Wemmenhove B., Mooij J.M., Wiegerinck W.A.J.J., Leisink M.A.R., Kappen H.J., Neijt J.P. file type imageInference in the promedas medical expert system Proceedings of the 11th Conference on Artificial Intelligence in Medicine (AIME 07), vol. 4594, pp. 456-460, 2007 bibtex

Marinazzo D., Kappen H.J., Gielen C.C.A.M. file type imageInput-driven oscillations in networs with excitatory and inhibitory neurons with dynamic synapses Neural Computation, vol. 19, pp. 1739-1765, 2007 bibtex

Mooij J.M., Wemmenhove B., Kappen H.J., Rizzo T. file type imageLoop corrected belief propagation Proceedings AISTATS 2007, vol. Website, pp. 8 pages, 2007 bibtex

Torres J.J., Cortes J.M., Marro J., Kappen H.J. file type imageCompetition between synaptic depression and facilitation in attractor neural networks Neural Computation, vol. 19, pp. 2739-2755, 2007 bibtex

Mooij J.M., Kappen H.J. file type imageSufficient conditions for convergence of the sum-product algorithm IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4422-4437, 2007 bibtex

Mooij J.M., Kappen H.J. file type imageLoop corrections for approximate inference on factor graphs Journal of Machine Learning Research, vol. 8, pp. 1113-1143, 2007 bibtex

Albers C.A., Kappen H.J. file type imageHaplotype inference in general pedigrees using the cluster variation method Genetics, no. 177, pp. 1101-1116, 2007 bibtex

2006
Cortes J.M., Torres J.J., Marro J., Garrido P.L., Kappen H.J. file type imageEffects of fast presynaptic noise in attractor neural networks Neural Computation, vol. 18, pp. 614-633, 2006 bibtex

Albers C.A., Leisink M.A.R., Kappen H.J. file type imageThe cluster variation method for efficient linkage analysis on extended pedigrees BMC Bioinformatics, Special issue on Machine Learning in Computational Biology, vol. 7(Suppl 1), pp. S1, 2006 bibtex

Wemmenhove B., Kappen H.J. file type imageSurvey propagation at finite temperature: application to a sourlas code as a toy model J. Phys. A: Math. Gen., vol. 39, pp. 1265-1283, 2006 bibtex

Cemgil A.T., Kappen H.J., Barber D. file type imageA generative model for music transcription IEEE Transactions on Speech and Audio Processing, vol. 14, pp. 679-694, 2006 bibtex

Mols B., Neijt J.P., Kappen H.J. file type imageComputer stelt straks medische diagnose: zeg eens @ Intermediair, pp. 51-53, 2006 bibtex

Wiegerinck W.A.J.J., Broek J.L. van den, Kappen H.J. file type imageStochastic optimal control in continuous space-time multi-agent systems UAI, vol. 22 th, pp. 528-535, 2006 bibtex

Kappen H.J., Mainzer K, Op zoek naar de ziel Zelfdenkende pillen en andere technologie die ons leven zal veranderen, pp. 217-223, 2006 bibtex

Torres J.J., Cortes J.M., Wemmenhove B., Marro J., Kappen H.J. Neural automata: the effect of microdynamics on unstable solutions 2006 bibtex

2005
Mooij J.M., Kappen H.J. file type imageSufficient conditions for convergence of loopy belief propagation Uncertainty in Artificial Intelligence, pp. 396-403, 2005 bibtex

Kappen H.J. file type imageIntelligente machines Inaugurele rede, pp. 1-23, 2005 bibtex

Mooij J.M., Kappen H.J. file type imageValidity estimates for loopy belief propagation on binary real-world networks Advances in Neural Information Processing Systems 17, vol. 17, pp. 945-952, 2005 bibtex

Cortes J.M., Garrido P.L., Kappen H.J., Marro J., Morrillas C, Navivad D file type imageAlgorithms for identification and categorization Proceedings of the AIP Conference, vol. 779, pp. 178-184, 2005 bibtex

Kappen H.J. file type imageA linear theory for control of non-linear stochastic systems Physical Review Letters, vol. 95, pp. 200201, 2005 bibtex

Kappen H.J. file type imagePath integrals and symmetry breaking for optimal control theory Journal of Statistical Mechanics: Theory and Experiment, pp. P11011, 2005 bibtex

Mooij J.M., Kappen H.J. file type imageOn the properties of the bethe approximation and loopy belief propagation on binary networks Journal of Statistical Mechanics: Theory and Experiment, pp. P110-12, 2005 bibtex

2004
Bakker B., Neijt J.P., Kappen H.J. file type imageImproving cox survival analysis with a neural-bayesian approach Statistics in Medicine, pp. 2989-3012, 2004 bibtex

2003
Heskes T.M., Albers C.A., Kappen H.J. file type imageApproximate inference and constrained optimization. In: Proceedings UAI-2003, pp. 313-320, 2003 bibtex

Cemgil A.T., Barber D., Kappen H.J. file type imageA dynamical bayesian network for tempo and polyphonic pitch tracking Proceedings of the International Conference on Artificial Neural Networks, pp. CD, 2003 bibtex

Pantic L., Torres J.J., Kappen H.J. file type imageCoincidence detection with dynamic synapses Network: Computation in Neural Systems, vol. 14, pp. 17-33, 2003 bibtex

Albers C.A., Kappen H.J. file type imageApplication of cluster variation method to genetic linkage analysis Proceedings BNAIC, vol. 15, pp. 11-18, 2003 bibtex

Torres J.J., Pantic L., Kappen H.J. file type imageOn the role of synaptic depression in the performance of attractor neural networks AIP Conference Proceedings 661, pp. 174-180, 2003 bibtex

Cemgil A.T., Kappen H.J. file type imageMonte carlo methods for tempo tracking and rhythm quantization Journal of Artificial Intelligence Research, vol. 18, pp. 45-81, 2003 bibtex

Leisink M.A.R., Kappen H.J. file type imageBound propagation Journal of Artificial Intelligence Research, pp. 139-154, 2003 bibtex

Cemgil A.T., Kappen H.J., Barber D. file type imageA generative model based polyphonic music transcription In Proceedings IEEE WASPAA, Workshop on Applications of Signal Processing to Audio and Acoustics., 2003 bibtex

2002
Kappen H.J., Neijt J.P. file type imagePromedas, a probabilistic decision support system for medical diagnosis SNN - UMCU, 2002 bibtex

Leisink M.A.R., Kappen H.J., Brunner H.G. file type imageLinkage analysis: a bayesian approach ICANN 2002, LNCS 2415, pp. 595-600, 2002 bibtex

Cemgil A.T., Kappen H.J. file type imageIntegrating tempo tracking and quantization using particle filtering Proceedings of 2002 International Computer Music Conference, Gothenburg/Sweden, pp. 419-422, 2002 bibtex

Kappen H.J., Wiegerinck W.A.J.J., Braak E.W.M.T. ter file type imageDecision support for medical diagnosis Dealing with the data flood. Mining data, text and multimedia, pp. 111-121, 2002 bibtex

Bakker B., Kappen H.J. file type imageApproximate algorithms for neural-bayesian approaches Theoretical Computer Science, vol. 287, no. 1, pp. 219-238, 2002 bibtex

Leisink M.A.R., Kappen H.J. file type imageMeans, correlations and bounds In: Advances in Neural Information Processing Systems, 14, vol. 14-1, pp. 455-462, 2002 bibtex

Cemgil A.T., Kappen H.J. Tempo tracking and rhythm quantization by sequential monte carlo Advances in Neural Information Processing Systems 14, Part VIII Applications, vol. 14-2, pp. 1361-1368, 2002 bibtex

Pantic L., Torres J.J., Kappen H.J., Gielen C.C.A.M. file type imageAssociative memory with dynamic synapses Neural Computation, vol. 14, pp. 2903-2923, 2002 bibtex

Kappen H.J. file type imageThe cluster variation method for approximate reasoning in medical diagnosis Modelling Bio-Medical Signals, pp. 3-16, 2002 bibtex

Kappen H.J., Wiegerinck W.A.J.J. file type imageNovel iteration schemes for the cluster variation method Advances in Neural Information Processing Systems 14, vol. 14, pp. 415-422, 2002 bibtex

Leisink M.A.R., Kappen H.J. file type imageGeneral lower bounds based on computer generated higher order expansions. In: Proceedings Uncertainty in AI 2002, vol. 18, pp. 293-210, 2002 bibtex

Torres J.J., Pantic L., Kappen H.J. file type imageOn the storage capacity of attractor neural networks with depressing synapses Physical Review E, vol. 66, pp. 061910, 2002 bibtex

2001
Cemgil A.T., Kappen H.J. file type imageBayesian real-time adaptation for interactive performance systems Proceedings of 2001 International Computer Music Conference, Havana/Cuba, pp. 147-150, 2001 bibtex

Kappen H.J., Wiegerinck W.A.J.J. file type imageSecond order approximations for probability models Advances in Neural Information Processing Systems 11, vol. 13, pp. 238-244, 2001 bibtex

Kappen H.J., Wiegerinck W.A.J.J., Nijman M.J. file type imageBayesbuilder Software Support for Bayesian Analysis Systems Proceedings, pp. 11-13, 2001 bibtex

Kappen H.J., Wiegerinck W.A.J.J. file type imageMean field theory for graphical models Advanced mean field theory, pp. 37-49, 2001 bibtex

Cemgil A.T., Kappen H.J., Desain P., Honing H. file type imageOn tempo tracking: tempogram representation and kalman filtering Journal of New Music Research, vol. 29, pp. 259-273, 2001 bibtex

Kappen H.J. An introduction to stochastic neural networks In: Handbook of Biological Physics, Neuro-informatics and Neural Modelling, vol. 4, pp. 517-552, 2001 bibtex

Leisink M.A.R., Kappen H.J. file type imageA tighter bound for graphical models Neural Computation, vol. 13, no. 9, pp. 2149--2171, 2001 bibtex

Kappen H.J. A novel iteration scheme for the cluster variation method Neural Information Processing Systems, vol. 13, pp. 415-422, 2001 bibtex

Kappen H.J., Gielen C.C.A.M., Wiegerinck W.A.J.J., Cemgil A.T., Nijman M.J. Approximate reasoning: real world applications of graphical models Foundations of Real-World Intelligence, pp. 73-121, 2001 bibtex

Cemgil A.T., Kappen H.J. A dynamic belief network implementation for realtime music transcription Proceedings of the 13th Belgian-Dutch Conference on Artificial Intelligence, pp. 473-474, 2001 bibtex

Torres J.J., Willems P.H.G.M., Kappen H.J., Koopman W.J.H. file type imageHysteresis and bistability in a realistic model for ip3-driven ca oscillations Europhysics Letters, vol. 55, no. 5, pp. 746-752, 2001 bibtex

Torres J.J., Kappen H.J., Willems P.H.G.M., Koopman W.J.H. file type imageMathematical model for calcium oscillations in non-excitable cells Biophysical Journal, vol. 80, pp. 613A, part 2, 2001 bibtex

2000
Leisink M.A.R., Kappen H.J. file type imageLinear response for higher order boltzmann machines Neural Networks, vol. 13 - 3, no. 3, pp. 329-335, 2000 bibtex

Kappen H.J. file type imageConstructing modular architectures with boltzmann machines Prerational Intelligence: Adaptive Behavior and Intelligent Systems Without Symbols and Logic., vol. 2, pp. 157-164, 2000 bibtex

Leisink M.A.R., Kappen H.J. file type imageA tighter bound for graphical models In Neural Information Processing Systems, NIPS 2000, Denver, USA, vol. 13, pp. 266-272, 2000 bibtex

Kappen H.J., Leisink M.A.R. file type imageKansrijke methoden voor kunstmatig leren en redeneren Ned tijdschrift voor Natuurkunde, vol. 66, pp. 14-19, 2000 bibtex

Cemgil A.T., Desain P., Kappen H.J. file type imageRhythm quantization for transcription Computer Music Journal, vol. 24, no. 2, pp. 60-75, 2000 bibtex

Wiegerinck W.A.J.J., Kappen H.J. file type imageApproximations of bayesian networks through kl minimisation New Generation Computing, vol. 18, no. 2, pp. 167-175, 2000 bibtex

Kappen H.J., Spanjers J.J. file type imageMean field theory for asymmetric neural networks Physical Review E, vol. 61, pp. 5658-5663, 2000 bibtex

Wiegerinck W.A.J.J., Kappen H.J., Leisink M.A.R., Barber D., Stroeve S. file type imageVariational methods for approximate reasoning in graphical models RWC'2000 symposium, Tokyo, Japan, pp. 265-270, 2000 bibtex

Bakker B., Kappen H.J. file type imageSurvival analysis: a neural-bayesian approach Proceedings Artificial Neural Networks in Medicine and Biology, pp. 162-167, 2000 bibtex

Cemgil A.T., Kappen H.J. A graphical model for music transcription In Neural Information Processing System, NIPS 2000, Denver, USA, 2000 bibtex

Wiegerinck W.A.J.J., Kappen H.J. file type imageA bridge between mean field theory and exact inference in probabilistic graphical models 2000 bibtex

Kappen H.J., Wiegerinck W.A.J.J. Stochastic dynamics with dominant self-coupling Presentation "Learning",, 2000 bibtex

Heskes T.M., Kappen H.J. An application of linear response learning Proceedings of the 12th Belgium-Netherlands Artificial Intelligence Conference, pp. 117-124, 2000 bibtex

Cemgil A.T., Kappen H.J., Desain P., Honing H. file type imageOn tempo tracking: tempogram representation and kalman filtering Int Computer Music Conf., Berlin, Sept. 2000, pp. 352-355, 2000 bibtex

Pantic L., Torres J.J., Kappen H.J. Coincidence detection with dynamic synapses Abstract Workshop NIPS'00, vol. 13, pp. workshop, 2000 bibtex

Pantic L., Torres J.J., Kappen H.J. file type imageOn the role of dynamical synapses in coincidence detection Proceedings of CNS'2000, Neurocomputing, vol. 38-40, pp. 285-291, 2000 bibtex

Kappen H.J. Predicting newspaper sales: jed system 'weathers' the tests Newspaper Techniques, 2000 bibtex

Leisink M.A.R., Kappen H.J. Learning higher order boltzmann machines using linear response Neural Networks, vol. 13, pp. 329--335, 2000 bibtex

Akaho S., Kappen H.J. file type imageNonmonotonic generalization bias of gaussian mixture models Ned tijdschrift voor Natuurkunde, vol. 12, pp. 1411�1427, 2000 bibtex

1999
Leisink M.A.R., Kappen H.J. file type imageValidity of tap equations in neural network Proceedings International Conference on Artificial Neural Networks 9, vol. 2, pp. 425-431, 1999 bibtex

Stroeve S., Kappen H.J., Gielen C.C.A.M. file type imageStimulus segmentation in a stochastic neural network with exogenous signals Proceedings International Conference on Artificial Neural Networks 9, vol. 2, pp. 732-737, 1999 bibtex

Wiegerinck W.A.J.J., Kappen H.J., Nijman M.J., Braak E.W.M.T. ter, Burg W.J.P.P. ter, Ying-Lie O. file type imageApproximate inference for medical diagnosis Pattern Recognition Letters, vol. 20, pp. 1231-1239, 1999 bibtex

Kappen H.J., Leisink M.A.R. On the validity of mean field theory for finite size boltzmann machines Proceedings Snowbird 99, Utah, USA, 1999 bibtex

Kappen H.J., Wiegerinck W.A.J.J., Nijman M.J. Promedas. a probabilistic medical diagnostic advisory system Presentatie project Promedas, 1999 bibtex

Cemgil A.T., Desain P., Kappen H.J. file type imageRhythm quantization for transcription Proceedings of the AISB'99 Convention, pp. 140-146, 1999 bibtex

Kappen H.J., Wiegerinck W.A.J.J., Nijman M.J., Braak E.W.M.T. ter, Burg W.J.P.P. ter, Ying-Lie O. Promedas. a probabilistic medical diagnostic advisory system AIM'99, pp. 16, 1999 bibtex

Kappen H.J. file type imageGlauber machines Technical Report SNN, NIPS'99 rejected, 1999 bibtex

Bakker B., Kappen H.J. A variational approach to bayesian survival analysis Advances in Neural Information Processing Systems 11, 1999 bibtex

Leisink M.A.R., Kappen H.J. file type imageAn application of linear response learning IJCNN'2000, 1999 bibtex

1998
Kappen H.J., Gielen C.C.A.M., Wiegerinck W.A.J.J., Barber D., Laar P. van de file type imageProbabilistic knowledge representation RWC'98, Tokyo, Japan, pp. 285-292, 1998 bibtex

Leisink M.A.R., Kappen H.J. file type imageLearning higher order boltzmann machines using linear response Artificial Neural Networks 8, vol. 2, pp. 511-517, 1998 bibtex

Akaho S., Kappen H.J. file type imageNonmonotonic generalization bias of gaussian mixture models Neural Computation, 1998 bibtex

Kappen H.J., Rodriquez F.B. file type imageBoltzmann machine learning using mean field theory and linear response correction Advances in Neural Information Processing Systems 10, pp. 280-286, 1998 bibtex

Kappen H.J., Nijman M.J., Moorsel T. van file type imageLearning active vision Industrial Applications of Neural Networks, pp. 193-202, 1998 bibtex

Kappen H.J., Groothof M. Just enough delivery INMA Ideas Magazine, pp. 23, 1998 bibtex

1997
Nijman M.J., Kappen H.J. Symmetry breaking and training from incomplete data with radial basis boltzmann machines International Journal of Neural Systems A, vol. 8, pp. 301-316, 1997 bibtex

Nijman M.J., Kappen H.J. Efficient learning in sparsely connected boltzmann machines Proceedings RWC'97, pp. 406-409, 1997 bibtex

Kappen H.J. file type imageStimulus dependent correlations in stochastic networks Physical Review E, vol. 55, pp. 5849-5858, 1997 bibtex

Kappen H.J., Rodriquez F.B. file type imageEfficient learning in boltzmann machines using linear response theory Neural Computation, vol. 10, pp. 1137-1156, 1997 bibtex

Kappen H.J., Rodriquez F.B. file type imageMean field approach to learning in boltzmann machines Pattern Recognition in Practice V, vol. 18, no. 11-13, pp. 1317-1322, 1997 bibtex

Wiegerinck W.A.J.J., Burg W.J.P.P. ter, Dam P.S. van, Ying-Lie O., Neijt J.P., Kappen H.J. An advisory system for anaemia based on boltzmann machines 5th Eurpean Congres on Intelligent Techniques and Soft Computing, vol. 1, pp. 364-368, 1997 bibtex

Kappen H.J. A polynomial time algorithm for boltzmann machine learning Workshop Cambridge, 1997 bibtex

Kappen H.J., Mempen, Dijken A. van, Otten H.A.F.M. Voorspelling van frisdrankverkoop 1997 bibtex

Wiegerinck W.A.J.J., Kappen H.J., Gielen C.C.A.M. file type imageMaking decisions with probability models 1997 bibtex

Kappen H.J., Gielen C.C.A.M. Neural networks: best practice in europe Neural Networks: Best Practice in Europe, pp. 209, 1997 bibtex

Kappen H.J., Rodriquez F.B. Accelerated learning in boltzmann machines using mean field theory Artificial Neural Networks 7, pp. 301-306, 1997 bibtex

Wiegerinck W.A.J.J., Burg W.J.P.P. ter, Dam P.S. van, Ying-Lie O., Neijt J.P., Kappen H.J. Lab-test selection in diagnosis of anaemia Neural Networks: Best Practice in Europe, pp. 179-181, 1997 bibtex

Wiegerinck W.A.J.J., Kappen H.J. Practical confidence and prediction intervals for prediction tasks Neural Networks: Best Practice in Europe, pp. 128-135, 1997 bibtex

Kappen H.J., Wiegerinck W.A.J.J., Morgan T., Harris T.J., Paillet G., Kopecz K. Stimulation initiative for european neural applications (siena) Neural Networks: Best Practice in Europe, pp. 1-8, 1997 bibtex

1996
unknown author: 168, Kappen H.J. file type imageNeural network analysis to predict outcome in patients with ovarian cancer Artificial Neural Networks 5, Session 1, pp. 433-436, 1996 bibtex

Tax D.M.J., Kappen H.J. file type imageLearning structure with many-take-all networks Artificial Neural Networks 6, pp. 95-101, 1996 bibtex

Nijman M.J., Kappen H.J. Efficient learning in sparsely connected boltzmann machines Artificial Neural Networks 6, pp. 41-46, 1996 bibtex

Kappen H.J. Classification with inquiry 1996 bibtex

unknown author: 192, Kappen H.J. Active decision Neural Networks, 1996 bibtex

Kappen H.J., Theeuwen M.M.H.J. Weersafhankelijkheid losse verkoop van kranten en tijdschriften in badplaatsen 1996 bibtex

Nijman M.J., Kappen H.J. Efficient learning in sparselyconnected boltzmann machines NIPS, 1996 bibtex

Wiegerinck W.A.J.J., Kappen H.J. Lab-test selection in diagnosis of anaemia Proceedings RWC, Japan, pp. 83-88, 1996 bibtex

Kappen H.J., Dijken A. van, Otten H.A.F.M. Voorspelling van verkoop en inzet van personeel 1996 bibtex

Nijman M.J., Kappen H.J. Efficient estimation of the partition function of anisotropic spin systems Physical Review Letters, 1996 bibtex

Kappen H.J., Verona P. file type imageDynamic feature linking in stochastic networks with short range interactions Artificial Neural Networks 6, pp. 101-106, 1996 bibtex

Wiegerinck W.A.J.J., Kappen H.J. file type imageDoorbraak neurale netwerken afhankelijk van standaardisatie Automatiserings Gids, vol. 30, pp. 17, 1996 bibtex

Wiegerinck W.A.J.J., Kappen H.J. Siena: stimulation initiative for european neural applications Proceedings EUFIT'96, pp. 280-281, 1996 bibtex

Kappen H.J. file type imageAn overview of neural network applications Proceedings 6th International Congress for Cumputer Technology in Agriculture, pp. 75-79, 1996 bibtex

Kappen H.J., Theeuwen M.M.H.J. Using neural networks to predict consumer behaviour Proceedings EUFIT'96, pp. 2149-2150, 1996 bibtex

1995
Kappen H.J. file type imageDeterministic learning rules for boltzmann machines Neural Networks, vol. 8, pp. 537-548, 1995 bibtex

unknown author: 183, Kappen H.J. Confidence intervals for neural networks 1995 bibtex

Kappen H.J., Nijman M.J., Motomura Y. file type imageRadial basis boltzmann machines and learning with missing values World Conference on Neural Network, vol. 1, pp. 72-75, 1995 bibtex

Kappen H.J., Pastoors A.G.W., Gielen C.C.A.M. Confidence intervals for neural networks Proceedings of the International Conference on Digital Signal Processing, vol. 1, pp. 396-401, 1995 bibtex

Heskes T.M., Kappen H.J. Self-organization and nonparametric regression Artificial Neural Networks 5, vol. 1, pp. 81-86, 1995 bibtex

Nijman M.J., Kappen H.J. Radial basis boltzmann machines and incomplete data 1995 bibtex

Kappen H.J., Nijman M.J. file type imageDynamic linking in stochastic networks Brain Processes, Theories and models. Proceedings W.S. McCullock: 25 years in memoriam., pp. 294-299, 1995 bibtex

Kappen H.J., Gielen C.C.A.M., Krose B.J.A. Active perception and cognition RWC'95, Tokyo, Japan, pp. 13-14, 1995 bibtex

Linders D., Kappen H.J. file type imageStochastic resonance and multimodal firing patterns in single-neuron models Neural networks: artificial intelligence and industrial applications, pp. 63-66, 1995 bibtex

Kappen H.J., Nijman M.J., Moorsel T. van Learning active vision: industrial application processing systems, Artificial Neural Networks 5, Session 7, Robotics, pp. 193-202, 1995 bibtex

Kappen H.J., Gielen C.C.A.M. Neural networks: artificial intelligence and industrial applications Proceedings of the 3rd SNN symposium, 1995 bibtex

Kappen H.J., Theeuwen M.M.H.J. Neurale netwerken en voorspelling losse verkoop 1995 bibtex

unknown author: 183, Kappen H.J. Automatisering van neurale netwerken; een direct-mailing applicatie 1995 bibtex

1994
Kappen H.J., Willems P. Neural network analysis to predict treatment outcome in patients with gynaecological cancer 1994 bibtex

Kappen H.J., Willems P. Using neural networks for survival prediction Proceedings Interregional Dutch-German Biometric Meeting, 1994 bibtex

Kappen H.J., Neijt J.P. Neural network analysis to predict treatment outcome in patients with gynaecological cancer 1994 bibtex

Kappen H.J. file type imageUsing boltzmann machines for probability estimation: a general framework for neural network learning Proceedings Pattern Recognition in Practice IV, pp. 299-312, 1994 bibtex

Laar P. van de, Kappen H.J. file type imageBoltzmann machines and the em algoritm 1994 bibtex

Nijman M.J., Kappen H.J. file type imageUsing boltzmann machines to fill in missing values 1994 bibtex

unknown author: 168, Gielen C.C.A.M., Daelemans W., Kappen H.J. Voorspelling samenstelling vliegas m.b.v neurale netwerken en symbolische methodes 1994 bibtex

unknown author: 168, Gielen C.C.A.M., Kappen H.J., Daelemans W. Korte termijn voorspelling van vliegas m.b.v. neurale netwerken en symbolsch inductie methodes 1994 bibtex

Kappen H.J. Neurale netwerken voor toepassingen op grote databases 1994 bibtex

unknown author: 168, Kappen H.J., Neijt J.P. Neural network analysis to predict treatment outcome in patients with gynaecological cancer 1994 bibtex

Gielen C.C.A.M., Kappen H.J. Neurale netwerken en hun toepassingen Informatie en Informatiebeleid, winter, vol. 12, no. 4, pp. 75-81, 1994 bibtex

1993
Heskes T.M., Kappen H.J. On-line learning processes in artificial neural networks vol. 51, pp. 199-234, 1993 bibtex

Kappen H.J., Neijt J.P. Neural network analysis to predict treatment outcome Annuals of Oncology, vol. 4, pp. 31-34, 1993 bibtex

Kappen H.J. Using boltzmann machines as perceptrons IEEE Trans. Neural Networks, 1993 bibtex

Wiegerinck W.A.J.J., Kappen H.J. Optimizing the architecture of multi-layer perceptrons for one-dimensional classification Artificial Neural Networks 3, pp. 558-561, 1993 bibtex

Kappen H.J. Using boltzmann machines for probability estimation Artificial Neural Networks 3, pp. 521-526, 1993 bibtex

Hal R. van, Kappen H.J., Neijt J.P. Neural network analysis for prediction of treatment outcome in ovarian cancer EWOC-3, 1993 bibtex

Opstal A.J. van, Kappen H.J. Neural representation of saccadic eye movements in monkey superior colliculus Artificial Neural Networks 3, pp. 88-93, 1993 bibtex

Heskes T.M., Kappen H.J. Learning processes in neural networks 1993 bibtex

Kappen H.J. Error potentials for self-organization International Conference on Neural Networks, San Francisco, vol. 3, pp. 1219-1223, 1993 bibtex

Slijpen E.T.P., Kappen H.J. Cooling schedules for learning in neural networks Physical Review E, vol. 47, pp. 4457-4464, 1993 bibtex

Kappen H.J. Neurale netwerken, fuzzy rules en artificiele intelligentie Klinische Fysica, vol. 1, pp. 13-16, 1993 bibtex

Opstal A.J. van, Kappen H.J. A two-dimensional model for spatial-temporal transformation of saccades in monkey superior colliculus Network, vol. 4, pp. 19-38, 1993 bibtex

Gielen C.C.A.M., Kappen H.J. Proceedings of the international confidence icann'93 Artificial Neural Networks 3, 1993 bibtex

1992
Slijpen E.T.P., Kappen H.J. Learning in neural networks with local minima Physical Review A, vol. 46, pp. 5221-5231, 1992 bibtex

Kappen H.J. Learning parameter adjustment in neural networks Physical Review A, vol. 45, pp. 8885-8893, 1992 bibtex

Kappen H.J. Learning rules, stochastic processes, and local minima Artificial Neural Networks 2, vol. 1, pp. 71-78, 1992 bibtex

Kappen H.J., Neijt J.P. Neural network analysis for prediction of treatment outcome in ovarian cancer ASCO, 1992 bibtex

Kappen H.J., Gielen C.C.A.M. Proceedings symposium on neural networks Proceedings of 2nd SNN Conference on Neural Networks, vol. 2, 1992 bibtex

1991
Heskes T.M., Kappen H.J. Learning processes in neural networks Physical Review A, vol. 44, pp. 2718-2726, 1991 bibtex

Kappen H.J., Gielen C.C.A.M. Neural networks learning in a changing environment Artificial Neural Networks 1, vol. 1, pp. 15-20, 1991 bibtex

Kappen H.J. Een computersimulatie van hetbilocale correlator model 1991 bibtex

Opstal A.J. van, Kappen H.J. Quantitative model for spatio-temporal transformation of oculomotor signals in monkey superior colliculus. Eur. J. Neurosci. (Suppl), vol. 14, pp. 56, 1991 bibtex

Kappen H.J. Neural networks learning in achanging environment International Joint Conference on Neural Networks, Seattle, vol. 1, pp. 823-828, 1991 bibtex

Kappen H.J. Learning at a constant rate 1991 bibtex

Kappen H.J. Neurale netwerken: verslag van een symposium Informatie, vol. 33, pp. 435-438, 1991 bibtex

Gielen C.C.A.M., Kappen H.J. Proceedings symposium on neural networks Proceedings of 1st SNN symposium, vol. 1, 1991 bibtex

1990
Kappen H.J., Gielen C.C.A.M. Neurocomputing research in thenetherlands Neurocomputing, vol. 2, pp. 35-38, 1990 bibtex

Latent kullback leibler control for continuous-state systems using probabilistic graphical models Proceedings UAI, vol. 30, bibtex


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Spaans Taalinstituut Marta Bela