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220613 ||| eng |
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|a 9789811904011
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|a Suzuki, Joe
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| 245 |
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|a Kernel Methods for Machine Learning with Math and Python
|h Elektronische Ressource
|b 100 Exercises for Building Logic
|c by Joe Suzuki
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| 250 |
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|a 1st ed. 2022
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| 260 |
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|a Singapore
|b Springer Nature Singapore
|c 2022, 2022
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| 300 |
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|a XII, 208 p. 32 illus., 29 illus. in color
|b online resource
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| 505 |
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|a Chapter 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
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| 653 |
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|a Artificial intelligence / Data processing
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| 653 |
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|a Machine learning
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| 653 |
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|a Statistical Learning
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| 653 |
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|a Machine Learning
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| 653 |
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|a Computational intelligence
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| 653 |
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|a Artificial Intelligence
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| 653 |
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|a Computational Intelligence
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| 653 |
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|a Artificial intelligence
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| 653 |
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|a Data Science
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 028 |
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|a 10.1007/978-981-19-0401-1
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| 856 |
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|u https://doi.org/10.1007/978-981-19-0401-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 006.3
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| 520 |
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|a The 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
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