01334oam 2200421Ia 450 991069752610332120080924130822.0(CKB)5470000002389694(OCoLC)44209790(EXLCZ)99547000000238969420000607d1998 ua 0engurmn|||||||||txtrdacontentcrdamediacrrdacarrierExplore the virtual side of earth science[electronic resource][Reston, Va.] :U.S. Dept. of the Interior, U.S. Geological Survey,[1998]1 unnumbered page digital, PDF fileUSGS fact sheet ;106-98Title from title screen (viewed on Sept. 23, 2008)."September 1998."Earth sciencesComputer simulationVRML (Computer program language)VolcanoesHawaiiSaint Helens, Mount (Wash.)Earth sciencesComputer simulation.VRML (Computer program language)VolcanoesGeological Survey (U.S.)GISGISOCLCQGPOBOOK9910697526103321Explore the virtual side of earth science3208157UNINA03071nam 2200733 a 450 991014457670332120200520144314.0978661100188997812810018871281001880978047014052904701405269780470140512047014051810.1002/9780470140529(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(Perlego)2763149(EXLCZ)99100000000037697320061117d2007 uy 0engur|n|---|||||txtccrLearning from data concepts, theory, and methods /Vladimir Cherkassky, Filip Mulier2nd ed.Hoboken, N.J. IEEE Press Wiley-Intersciencec20071 online resource (558 p.)Description based upon print version of record.9780471681823 0471681822 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.006.3/1Cherkassky Vladimir S104932Mulier Filip145715MiAaPQMiAaPQMiAaPQBOOK9910144576703321Learning from data835380UNINA