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008			221112s2022 si | s |||| 0|eng d
020			_a9789811903984
024	7		_a10.1007/978-981-19-0398-4 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2022 EB
100	1		_aSuzuki, Joe _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681524
245	1	0	_aKernel Methods for Machine Learning with Math and R : _b100 Exercises for Building Logic _cby Joe Suzuki
250			_aFirst edition 2022
264		1	_aSingapore _bSpringer International Publising _c2022
300			_a1 recurso en línea (XII, 196 páginas) _b32 ilustraciones, 29 ilustraciones a color
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
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.
988			_aSpringer_Computer_2022
650		7	_2embne _9166090 _aAprendizaje automático _xMatemáticas
650		7	_2embne _9139136 _aLógica matemática
776	0	8	_iPrinted edition: _z9789811903977
776	0	8	_iPrinted edition: _z9789811903991
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-0398-4 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b11/2022 _dz _eIG _zSI