LEADER 03018nam 2200433 450 001 996475762603316 005 20221204134625.0 010 $a9789811904011$b(electronic bk.) 010 $z9789811904004 035 $a(MiAaPQ)EBC6986761 035 $a(Au-PeEL)EBL6986761 035 $a(CKB)22371876200041 035 $a(PPN)269151966 035 $a(EXLCZ)9922371876200041 100 $a20221204d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aKernel methods for machine learning with math and python $e100 exercises for building logic /$fJoe Suzuki 210 1$aGateway East, Singapore :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (216 pages) 311 08$aPrint version: Suzuki, Joe Kernel Methods for Machine Learning with Math and Python Singapore : Springer,c2022 9789811904004 320 $aIncludes bibliographical references and index. 327 $aIntro -- Preface -- How to Overcome Your Kernel Weakness -- What Makes KMMP Unique? -- Acknowledgments -- Contents -- 1 Positive Definite Kernels -- 1.1 Positive Definiteness of a Matrix -- 1.2 Kernels -- 1.3 Positive Definite Kernels -- 1.4 Probability -- 1.5 Bochner's Theorem -- 1.6 Kernels for Strings, Trees, and Graphs -- Appendix -- Exercises 1 sim 15 -- 2 Hilbert Spaces -- 2.1 Metric Spaces and Their Completeness -- 2.2 Linear Spaces and Inner Product Spaces -- 2.3 Hilbert Spaces -- 2.4 Projection Theorem -- 2.5 Linear Operators -- 2.6 Compact Operators -- Appendix: Proofs of Propositions -- Exercises 16 sim 30 -- 3 Reproducing Kernel Hilbert Space -- 3.1 RKHSs -- 3.2 Sobolev Space -- 3.3 Mercer's Theorem -- Appendix -- Exercises 31 sim 45 -- 4 Kernel Computations -- 4.1 Kernel Ridge Regression -- 4.2 Kernel Principle Component Analysis -- 4.3 Kernel SVM -- 4.4 Spline Curves -- 4.5 Random Fourier Features -- 4.6 Nyström Approximation -- 4.7 Incomplete Cholesky Decomposition -- Appendix -- Exercises 46 sim 64 -- 5 The MMD and HSIC -- 5.1 Random Variables in RKHSs -- 5.2 The MMD and Two-Sample Problem -- 5.3 The HSIC and Independence Test -- 5.4 Characteristic and Universal Kernels -- 5.5 Introduction to Empirical Processes -- Appendix -- Exercises 65 sim83 -- 6 Gaussian Processes and Functional Data Analyses -- 6.1 Regression -- 6.2 Classification -- 6.3 Gaussian Processes with Inducing Variables -- 6.4 Karhunen-Lóeve Expansion -- 6.5 Functional Data Analysis -- Appendix -- Exercises 83sim100 -- Appendix Bibliography. 606 $aArtificial intelligence 606 $aArtificial intelligence$xData processing 615 0$aArtificial intelligence. 615 0$aArtificial intelligence$xData processing. 676 $a515.9 700 $aSuzuki$b Joe$0846228 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a996475762603316 996 $aKernel Methods for Machine Learning with Math and Python$92851093 997 $aUNISA LEADER 01111nam a22002651i 4500 001 991002374369707536 005 20030623164632.0 008 030925s1909 it |||||||||||||||||ita 035 $ab12279870-39ule_inst 035 $aARCHE-032744$9ExL 040 $aBiblioteca Interfacoltà$bita$cA.t.i. 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Bortrone e C.,$c1909 300 $a1 v. ;$c25 cm 500 $aIn testa al front.: Tribunale civile di Lecce, 2. sezione 500 $aSul front.: Oggetto: Azione in riconvenzione, Eccezione riconvenzionale, Compensazione, Tassa di registro, Surroga, Privilegio 650 4$aLecce$xTribunale civile$xControversie 710 1 $aItalia :$bTribunale$c 907 $a.b12279870$b02-04-14$c08-10-03 912 $a991002374369707536 945 $aLE002 Busta B 80/14$g1$i2002000776723$lle002$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i12672361$z08-10-03 996 $aMarzo Stella contro Trazza Luigi$9165042 997 $aUNISALENTO 998 $ale002$b08-10-03$cm$da $e-$fita$git $h0$i1 LEADER 01842nam 2200421Ia 450 001 9910697893703321 005 20090217144811.0 035 $a(CKB)5470000002392042 035 $a(OCoLC)276449506 035 $a(EXLCZ)995470000002392042 100 $a20081126d2007 ua 0 101 0 $aeng 135 $aurcn||||m|||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aSpent fuel transportation package response to the Baltimore Tunnel fire scenario$b[electronic resource] $efinal report /$fprepared by H.E. Adkins, Jr. ... [and others] 205 $aRev. 1. 210 1$aWashington, DC :$cDivision of Spent Fuel Storage and Transportation, Office of Nuclear Material Safety and Safeguards, U.S. Nuclear Regulatory Commission,$d[2007] 215 $a1 electronic text (1 volumes (various pagings)) $cHTML, digital, PDF file 300 $aTitle from title screen (viewed on Nov. 26, 2008). 300 $a"Pacific Northwest National Laboratory." 300 $a"Date published: November 2006." 300 $a"NUREG/CR-6886." 300 $a"PNNL-15313." 320 $aIncludes bibliographical references (page 9.1 - 9.3). 517 $aSpent fuel transportation package response to the Baltimore Tunnel fire scenario 606 $aSpent reactor fuels$xTransportation$xSafety measures 615 0$aSpent reactor fuels$xTransportation$xSafety measures. 701 $aAdkins$b H. 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