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020 _a9789811904011
_9978-981-19-0401-1
024 7 _a10.1007/978-981-19-0401-1
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
072 7 _aUYQ
_2thema
082 0 4 _a006.3
_223
100 1 _aSuzuki, Joe.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aKernel Methods for Machine Learning with Math and Python
_h[electronic resource] :
_b100 Exercises for Building Logic /
_cby Joe Suzuki.
250 _a1st ed. 2022.
264 1 _aSingapore :
_bSpringer Nature Singapore :
_bImprint: Springer,
_c2022.
300 _aXII, 208 p. 32 illus., 29 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1: Positive Definite Kernels -- Chapter 2: Hilbert Spaces -- Chapter 3: Reproducing Kernel Hilbert Space -- Chapter 4: Kernel Computations -- Chapter 5: MMD and HSIC -- Chapter 6: Gaussian Processes and Functional Data Analyses.
520 _aThe most crucial ability for machine learning and data science is mathematical logic for grasping their essence rather than relying on knowledge or experience. This textbook addresses the fundamentals of kernel methods for machine learning by considering relevant math problems and building Python programs. The book’s main features are as follows: The content is written in an easy-to-follow and self-contained style. The book includes 100 exercises, which have been carefully selected and refined. As their solutions are provided in the main text, readers can solve all of the exercises by reading the book. The mathematical premises of kernels are proven and the correct conclusions are provided, helping readers to understand the nature of kernels. Source programs and running examples are presented to help readers acquire a deeper understanding of the mathematics used. Once readers have a basic understanding of the functional analysis topicscovered in Chapter 2, the applications are discussed in the subsequent chapters. Here, no prior knowledge of mathematics is assumed. This book considers both the kernel for reproducing kernel Hilbert space (RKHS) and the kernel for the Gaussian process; a clear distinction is made between the two.
650 0 _aArtificial intelligence.
650 0 _aMachine learning.
650 0 _aComputational intelligence.
650 0 _aArtificial intelligence
_xData processing.
650 1 4 _aArtificial Intelligence.
650 2 4 _aMachine Learning.
650 2 4 _aStatistical Learning.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Science.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9789811904004
776 0 8 _iPrinted edition:
_z9789811904028
856 4 0 _uhttps://doi.org/10.1007/978-981-19-0401-1
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
942 _cSPRINGER
999 _c178020
_d178020