| Vladimir Vapnik - Computers - 2000 - 314 pages
Four periods in the research of the learning problem. Setting of the learning problem. Informal reasoning and comments. Consistency of learning processes. Bounds on the rate of ... | |
| Sumio Watanabe - Computers - 2009 - 300 pages
Sure to be influential, this book lays the foundations for the use of algebraic geometry in statistical learning theory. Many widely used statistical models and learning ... | |
| Sanjeev Kulkarni, Gilbert Harman - Mathematics - 2011 - 288 pages
A thought-provoking look at statistical learning theory and its role in understanding human learning and inductive reasoning A joint endeavor from leading researchers in the ... | |
| Jorge E. Hurtado - Computers - 2004 - 257 pages
The last decades have witnessed the development of methods for solving struc tural reliability problems, which emerged from the efforts of numerous re searchers all over the ... | |
| Alexander J. Smola - Computers - 2000 - 412 pages
The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths ... | |
| Vladimir Cherkassky, Filip M. Mulier - Computers - 2007 - 624 pages
An interdisciplinary framework for learning methodologies—covering statistics, neural networks, and fuzzy logic, this book provides a unified treatment of the principles and ... | |
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