Unsupervised Clustering in Hough Space for Identification of Partially Occluded Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Selecting relevant instances for efficient and accurate collaborative filtering
Proceedings of the tenth international conference on Information and knowledge management
Beyond second-order statistics for learning: A pairwise interaction model for entropy estimation
Natural Computing: an international journal
Learning from Examples with Information Theoretic Criteria
Journal of VLSI Signal Processing Systems
On-line learning in changing environments with applications in supervised and unsupervised learning
Neural Networks - Computational models of neuromodulation
Dictionary learning algorithms for sparse representation
Neural Computation
Probabalistic Models and Informative Subspaces for Audiovisual Correspondence
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Optimal Extraction of Hidden Causes
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
An Improved Cumulant Based Method for Independent Component Analysis
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A Comparative Study of ICA Filter Structures Learnt from Natural and Urban Images
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Lower and Upper Bounds for Misclassification Probability Based on Renyi's Information
Journal of VLSI Signal Processing Systems
The Minimum Error Minimax Probability Machine
The Journal of Machine Learning Research
Multidimensional Encoding Strategy of Spiking Neurons
Neural Computation
Constructive Incremental Learning from Only Local Information
Neural Computation
Task Switching and Novelty Processing Activate a Common Neural Network for Cognitive Control
Journal of Cognitive Neuroscience
Sequential Data Fusion via Vector Spaces: Fusion of Heterogeneous Data in the Complex Domain
Journal of VLSI Signal Processing Systems
Semilinear predictability minimization produces well-known feature detectors
Neural Computation
Automatic real time localization of frowning and smiling faces under controlled head rotations
WSEAS Transactions on Signal Processing
Reduction of noise due to systematic uncertainties in 113mIn SPECT imaging using information theory
Computers in Biology and Medicine
Artificial Intelligence in Medicine
Tools for application-driven linear dimension reduction
Neurocomputing
MUSP'06 Proceedings of the 6th WSEAS international conference on Multimedia systems & signal processing
Prototype based classification using information theoretic learning
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
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From the Publisher:Neural networks provide a powerful new technology to model and control nonlinear and complex systems. In this book, the authors present a detailed formulation of neural networks from the information-theoretic viewpoint. They show how this perspective provides new insights into the design theory of neural networks. In particular, they show how these methods may be applied to the topics of supervised and unsupervised learning, including feature extraction, linear and nonlinear independent component analysis, and Boltzmann machines. Readers are assumed to have a basic understanding of neural networks, but all of the relevant concepts from information theory are carefully introduced and explained. Consequently, readers from several different scientific disciplines - notably, cognitive scientists, engineers, physicists, statisticians, and computer scientists - will find this book to be a very valuable contribution to this topic.