000			02919cam a2200325 a 4500
008			090412s1998 nyua b 001 0 eng
010			_a97043019
020			_a9780471154938
035			_a(Sirsi) u1401
040			_aEG-CaNU _cEG-CaNU _dEG-CaNU
042			_ancode
082	0	0	_a006.31 _2 21
100	1		_aCherkassky, Vladimir S. _91505
245	1	0	_aLearning from data : _b concepts, theory, and methods / _c Vladimir Cherkassky, Filip Mulier.
260			_aNew York : _b Wiley, _c c1998.
300			_axviii, 441 p. : _b ill. ; _c 25 cm.
500			_a"A Wiley-Interscience publication."
504			_aIncludes bibliographical references and index.
505	0		_aProblem Statement, Classical Approaches, and Adaptive Learning -- Regularization Framework -- Statistical Learning Theory -- Nonlinear Optimization Strategies -- Methods for Data Reduction and Dimensionality Reduction -- Methods for Regression -- Classification -- Support Vector Machines -- Fuzzy Systems -- Appendices -- Index.
520			_a An interdisciplinary framework for learning methodologies-covering statistics, neural networks, and fuzzy logic This book provides a unified treatment of the principles and methods for learning dependencies from data. It establishes a general conceptual framework in which various learning methods from statistics, neural networks, and fuzzy logic can be applied-showing that a few fundamental principles underlie most new methods being proposed today in statistics, engineering, and computer science. Complete with over one hundred illustrations, case studies, and examples, Learning from Data: * Relates statistical formulation with the latest methodologies used in artificial neural networks, fuzzy systems, and wavelets * Features consistent terminology, chapter summaries, and practical research tips * Emphasizes the conceptual framework provided by Statistical Learning Theory (VC-theory) rather than its commonly practiced mathematical aspects * Provides a detailed description of the new learning methodology called Support Vector Machines (SVM) This invaluable text/reference accommodates both beginning and advanced graduate students in engineering, computer science, and statistics. It is also indispensable for researchers and practitioners in these areas who must understand the principles and methods for learning dependencies from data.
650		0	_aAdaptive signal processing. _91506
650		0	_aMachine learning. _9107
650		0	_aNeural networks (Computer science) _91507
650		0	_aFuzzy systems. _91508
700	1		_aMulier, Filip. _91509
596			_a1
999			_c447 _d447