Complexity of learning in artificial neural networks
Theoretical Computer Science - Phase transitions in combinatorial problems
Statistical dynamics of on-line independent component analysis
The Journal of Machine Learning Research
Statistical dynamics of on-line independent component analysis
The Journal of Machine Learning Research
Performance analysis of LVQ algorithms: a statistical physics approach
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Dynamics and Generalization Ability of LVQ Algorithms
The Journal of Machine Learning Research
Phase transitions in vector quantization and neural gas
Neurocomputing
Statistical Mechanics of On-line Learning
Similarity-Based Clustering
Large memory capacity in chaotic artificial neural networks: a view of the anti-integrable limit
IEEE Transactions on Neural Networks
Improved security of neural cryptography using don't-trust-my-partner and error prediction
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Window-based example selection in learning vector quantization
Neural Computation
IEEE Transactions on Neural Networks
Node perturbation learning without noiseless baseline
Neural Networks
Intelligence and embodiment: A statistical mechanics approach
Neural Networks
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From the Publisher:The effort to build machines that are able to learn and undertake tasks such as datamining, image processing and pattern recognition has led to the development of artificial neural networks in which learning from examples may be described and understood. The contribution to this subject made over the past decade by researchers applying the techniques of statistical mechanics is the subject of this book. The authors provide a coherent account of various important concepts and techniques that are currently only found scattered in papers, supplement this with background material in mathematics and physics, and include many examples and exercises.