CoFD: An Algorithm for Non-distance Based Clustering in High Dimensional Spaces
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Algorithms for clustering high dimensional and distributed data
Intelligent Data Analysis
Proceedings of the 2009 conference on New Directions in Neural Networks: 18th Italian Workshop on Neural Networks: WIRN 2008
Cortical circuitry implementing graphical models
Neural Computation
Notes on Cutset Conditioning on Factor Graphs with Cycles
Proceedings of the 2009 conference on Neural Nets WIRN09: Proceedings of the 19th Italian Workshop on Neural Nets, Vietri sul Mare, Salerno, Italy, May 28--30 2009
Tracking complex objects using graphical object models
IWCM'04 Proceedings of the 1st international conference on Complex motion
Maximum a posteriori estimation of dynamically changing distributions
KI'09 Proceedings of the 32nd annual German conference on Advances in artificial intelligence
Implementing First-Order Variables in a Graphical Cognitive Architecture
Proceedings of the 2010 conference on Biologically Inspired Cognitive Architectures 2010: Proceedings of the First Annual Meeting of the BICA Society
PAMPAS: real-valued graphical models for computer vision
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Coupling bayesian networks with GIS-Based cellular automata for modeling land use change
GIScience'06 Proceedings of the 4th international conference on Geographic Information Science
Robots that learn language: developmental approach to human-machine conversations
EELC'06 Proceedings of the Third international conference on Emergence and Evolution of Linguistic Communication: symbol Grounding and Beyond
Loose-limbed People: Estimating 3D Human Pose and Motion Using Non-parametric Belief Propagation
International Journal of Computer Vision
Causal conditioning and instantaneous coupling in causality graphs
Information Sciences: an International Journal
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From the Publisher:Graphical models use graphs to represent and manipulate joint probability distributions. They have their roots in artificial intelligence, statistics, and neural networks. The clean mathematical formalism of the graphical models framework makes it possible to understand a wide variety of network-based approaches to computation, and in particular to understand many neural network algorithms and architectures as instances of a broader probabilistic methodology. It also makes it possible to identify novel features of neural network algorithms and architectures and to extend them to more general graphical models. This book exemplifies the interplay between the general formal framework of graphical models and the exploration of new algorithms and architectures. The articles, which are drawn from the journal Neural Computation, range from foundational papers of historical importance to results at the cutting edge of research.