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008 110609s2011 njua ob 001 0 eng d
020 _a9781118023471
_q(electronic bk.)
020 _a1118023471
_q(electronic bk.)
020 _a9781118023433
_q(e-book)
020 _a1118023439
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020 _a1283098687
020 _a9781283098687
020 _z9780470641835
020 _z9781118023464
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020 _z0470641835
020 _z1118023463
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035 _a(OCoLC)729724626
_z(OCoLC)726329153
_z(OCoLC)732956377
_z(OCoLC)741451118
_z(OCoLC)754717771
_z(OCoLC)778448327
_z(OCoLC)816840042
037 _a10.1002/9781118023471
_bWiley InterScience
_nhttp://www3.interscience.wiley.com
040 _aDG1
_beng
_epn
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049 _aMAIN
050 4 _aQ325.5
_b.K85 2011
060 4 _aQ 325.5
072 7 _aCOM
_x005030
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072 7 _aCOM
_x004000
_2bisacsh
082 0 4 _a006.3/1
_222
100 1 _aKulkarni, Sanjeev.
245 1 3 _aAn elementary introduction to statistical learning theory /
_cSanjeev Kulkarni, Gilbert Harman.
_h[electronic resource]
260 _aHoboken, N.J. :
_bWiley,
_c©2011.
300 _a1 online resource (1 volume) :
_billustrations.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
490 1 _aWiley series in probability and statistics
504 _aIncludes bibliographical references and index.
505 0 _aIntroduction: Classification, Learning, Features, and Applications -- Probability -- Probability Densities -- The Pattern Recognition Problem -- The Optimal Bayes Decision Rule -- Learning from Examples -- The Nearest Neighbor Rule -- Kernel Rules -- Neural Networks: Perceptrons -- Multilayer Networks -- PAC Learning -- VC Dimension -- Infinite VC Dimension -- The Function Estimation Problem -- Learning Function Estimation -- Simplicity -- Support Vector Machines -- Boosting.
520 _a"A joint endeavor from leading researchers in the fields of philosophy and electrical engineering An Introduction to Statistical Learning Theory provides a broad and accessible introduction to rapidly evolving field of statistical pattern recognition and statistical learning theory. Exploring topics that are not often covered in introductory level books on statistical learning theory, including PAC learning, VC dimension, and simplicity, the authors present upper-undergraduate and graduate levels with the basic theory behind contemporary machine learning and uniquely suggest it serves as an excellent framework for philosophical thinking about inductive inference"--Back cover.
588 0 _aPrint version record.
650 0 _aMachine learning
_xStatistical methods.
650 0 _aPattern recognition systems.
650 2 _aArtificial Intelligence.
650 2 _aPattern Recognition, Automated.
650 2 _aStatistics as Topic.
650 0 4 _aAprenentatge automàtic
_xMètodes estadístics.
650 4 _aReconeixement de formes (Informàtica)
650 7 _aCOMPUTERS
_xEnterprise Applications
_xBusiness Intelligence Tools.
_2bisacsh
650 7 _aCOMPUTERS
_xIntelligence (AI) & Semantics.
_2bisacsh
650 7 _aMachine learning
_xStatistical methods.
_2fast
_0(OCoLC)fst01004801
650 7 _aPattern recognition systems.
_2fast
_0(OCoLC)fst01055266
650 0 7 _aMaschinelles Lernen.
_0(DE-588c)4193754-5
_2swd
650 0 7 _aStatistik.
_0(DE-588c)4056995-0
_2swd
655 4 _aLlibres electrònics.
655 4 _aElectronic books.
700 1 _aHarman, Gilbert.
710 2 _aWiley InterScience (Online service)
776 0 8 _iPrint version:
_aKulkarni, Sanjeev.
_tElementary introduction to statistical learning theory.
_dHoboken, N.J. : Wiley, ©2011
_z9781118023471
_w(OCoLC)726329153
830 0 _aWiley series in probability and statistics.
856 4 0 _uhttp://onlinelibrary.wiley.com/book/10.1002/9781118023471
_zWiley Online Library
942 _2ddc
_cBK
999 _c205074
_d205074