000 | 03374nam a22005535i 4500 | ||
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001 | 978-981-19-0398-4 | ||
003 | DE-He213 | ||
005 | 20240423125531.0 | ||
007 | cr nn 008mamaa | ||
008 | 220503s2022 si | s |||| 0|eng d | ||
020 |
_a9789811903984 _9978-981-19-0398-4 |
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024 | 7 |
_a10.1007/978-981-19-0398-4 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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072 | 7 |
_aCOM004000 _2bisacsh |
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072 | 7 |
_aUYQ _2thema |
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082 | 0 | 4 |
_a006.3 _223 |
100 | 1 |
_aSuzuki, Joe. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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245 | 1 | 0 |
_aKernel Methods for Machine Learning with Math and R _h[electronic resource] : _b100 Exercises for Building Logic / _cby Joe Suzuki. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aSingapore : _bSpringer Nature Singapore : _bImprint: Springer, _c2022. |
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300 |
_aXII, 196 p. 32 illus., 29 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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_atext file _bPDF _2rda |
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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 R 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 topics covered 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 |
_aArtificial intelligence _xData processing. |
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650 | 0 | _aComputational intelligence. | |
650 | 1 | 4 | _aArtificial Intelligence. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aStatistical Learning. |
650 | 2 | 4 | _aData Science. |
650 | 2 | 4 | _aComputational Intelligence. |
710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9789811903977 |
776 | 0 | 8 |
_iPrinted edition: _z9789811903991 |
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-981-19-0398-4 |
912 | _aZDB-2-SCS | ||
912 | _aZDB-2-SXCS | ||
942 | _cSPRINGER | ||
999 |
_c178989 _d178989 |