| Vladimir Naumovich Vapnik - Computers - 1998 - 736 pages
A comprehensive look at learning and generalization theory. The statistical theory of learning and generalization concerns the problem of choosing desired functions on the ... | |
| 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 ... | |
| V. Vapnik, S. Kotz - Mathematics - 2006 - 523 pages
Provides the classical foundation of Statistical Learning Theory. Divided into two parts, this book covers a spectrum of ideas related to the essence of intelligence: from the ... | |
| Terrence L. Fine - Computers - 1999 - 340 pages
The decade prior to publication has seen an explosive growth in com- tational speed and memory and a rapid enrichment in our understa- ing of arti?cial neural networks. These ... | |
| 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 ... | |
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