000			02043nam a22002777a 4500
005			20220901115758.0
008			220330b |||||||| |||| 00| 0 eng d
020			_a9788126528929
028			_a:Technical Bureau India Pvt. Ltd. _b:E/261, Shastri Nagar Delhi _c:TB5811 _d:25/03/2022
082			_a519.5 VAP
100			_aVapnik, Vladimir N
245			_aStatistical Learning Theory _c/ Vladimir N Vapnik
260			_aNew Delhi _b: Wiley, _c©1998.
300			_axxiv, 736p. _c24cm
365			_b2595
490			_aAdaptive and learning systems for signal processing, communications, and control.
505			_aPartial 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.
520			_aThis 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.
650			_aMathematics
650			_aProbabilities and applied mathematics
650			_aComputational learning theory.
650			_aMathematische Lerntheorie
650			_aApprentissage automatique.
700			_aVapnik, Vladimir N
942			_cBOOK
999			_c58979 _d58979