Nonparametric Time Series Prediction Through Adaptive ModelSelection
Machine Learning
Intelligent systems: architectures and perspectives
Recent advances in intelligent paradigms and applications
Almost Linear VC-Dimension Bounds for Piecewise Polynomial Networks
Neural Computation
Learning to identify winning coalitions in the PAC model
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Aspects of discrete mathematics and probability in the theory of machine learning
Discrete Applied Mathematics
Learning from uniformly ergodic Markov chains
Journal of Complexity
Mechanisms for partial information elicitation: the truth, but not the whole truth
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Joint universal lossy coding and identification of stationary mixing sources with general alphabets
IEEE Transactions on Information Theory
Indexability, concentration, and VC theory
Proceedings of the Third International Conference on SImilarity Search and APplications
Unsupervised slow subspace-learning from stationary processes
ALT'06 Proceedings of the 17th international conference on Algorithmic Learning Theory
The generalization performance of learning machine with NA dependent sequence
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Indexability, concentration, and VC theory
Journal of Discrete Algorithms
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From the Publisher:How does it differ from first edition? Includes new material on: * support vector machines (SVM's), * fat shattering dimensions * applications to neural network learning, * learning with dependent samples generated by beta-mixing process, * connections between system identification and learning theory * probabilistic solution of "intractable" problems in robust control and matrix theory using randomised algorithms. In addition, solutions to some open problems posed in the first edition are included, and new open problems are added. The author is a respected authority in the field of control and systems theory. This new edition, with substantial new material, takes account of important new developments in the theory of learning. It also deals extensively with the theory of learning control systems, which has now reached a level of maturity comparable to that of learning of neural networks. The book is written in a manner that would suit self-study and contains comprehensive references. The chapters are also written to be as autonomous as possible and contain updated open problems to enhance further research and self-study.