02043nam a22002777a 4500 20220901115758.0 220330b |||||||| |||| 00| 0 eng d 9788126528929 :Technical Bureau India Pvt. Ltd. :E/261, Shastri Nagar Delhi :TB5811 :25/03/2022 519.5 VAP Vapnik, Vladimir N Statistical Learning Theory / Vladimir N Vapnik New Delhi : Wiley, ©1998. xxiv, 736p. 24cm 2595 Adaptive and learning systems for signal processing, communications, and control. Partial table of contents:; THEORY OF LEARNING AND GENERALIZATION.; Two Approaches to the Learning Problem.; Estimation of the Probability Measure and Problem of Learning.; Conditions for Consistency of Empirical Risk Minimization Principle.; The Structural Risk Minimization Principle.; Stochastic Ill--Posed Problems.; SUPPORT VECTOR ESTIMATION OF FUNCTIONS.; Perceptrons and Their Generalizations.; SV Machines for Function Approximations, Regression Estimation, and Signal Processing.; STATISTICAL FOUNDATION OF LEARNING THEORY.; Necessary and Sufficient Conditions for Uniform Convergence of Frequencies to Their Probabilities.; Necessary and Sufficient Conditions for Uniform One--Sided Convergence of Means to Their Expectations.; Comments and Bibliographical Remarks.; References.; Index. This book is devoted to the statistical theory of learning and generalization, that is, the problem of choosing the desired function on the basis of empirical data. The author will present the whole picture of learning and generalization theory. Learning theory has applications in many fields, such as psychology, education and computer science. Mathematics Probabilities and applied mathematics Computational learning theory. Mathematische Lerntheorie Apprentissage automatique. Vapnik, Vladimir N BOOK 58979 58979 0 0 ddc 0 1 SNUL SNUL 2022-03-30 Technical Bureau India Pvt. Ltd. 2595.00 519.5 VAP 28570 2022-03-30 00:00:00 2022-03-30 BOOK Books Shifted in Mathematics Dept.