02947nam 2200685 450 991014457670332120230125183541.01-281-00188-097866110018890-470-14052-60-470-14051-810.1002/9780470140529(CKB)1000000000376973(EBL)313393(SSID)ssj0000190532(PQKBManifestationID)11189217(PQKBTitleCode)TC0000190532(PQKBWorkID)10179772(PQKB)10250076(MiAaPQ)EBC313393(CaBNVSL)mat05201503(IDAMS)0b0000648104a999(IEEE)5201503(OCoLC)181345815(PPN)273206834(EXLCZ)99100000000037697320101007h20152007 uy 0engur|n|---|||||txtccrLearning from data concepts, theory, and methods /Vladimir Cherkassky, Filip Mulier2nd ed.Hoboken, New Jersey :IEEE Press :c2007.1 online resource (558 p.)Description based upon print version of record.0-471-68182-2 Includes bibliographical references (p. 519-531) and index.Problem 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 -- Noninductive inference and alternative learning formulations.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 making this an invaluable text.Adaptive signal processingMachine learningNeural networks (Computer science)Fuzzy systemsAdaptive signal processing.Machine learning.Neural networks (Computer science)Fuzzy systems.006.31006.32Cherkassky Vladimir S104932Mulier Filip145715CaBNVSLCaBNVSLCaBNVSLBOOK9910144576703321Learning from data835380UNINA