Kernel methods for machine learning with math and python : 100 exercises for building logic / / Joe Suzuki
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| Autore: |
Suzuki Joe
|
| Titolo: |
Kernel methods for machine learning with math and python : 100 exercises for building logic / / Joe Suzuki
|
| Pubblicazione: | Gateway East, Singapore : , : Springer, , [2022] |
| ©2022 | |
| Descrizione fisica: | 1 online resource (216 pages) |
| Disciplina: | 515.9 |
| Soggetto topico: | Artificial intelligence |
| Artificial intelligence - Data processing | |
| Nota di bibliografia: | Includes bibliographical references and index. |
| Nota di contenuto: | Intro -- 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. |
| Titolo autorizzato: | Kernel Methods for Machine Learning with Math and Python |
| ISBN: | 9789811904011 |
| 9789811904004 | |
| Formato: | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione: | Inglese |
| Record Nr.: | 996475762603316 |
| Lo trovi qui: | Univ. di Salerno |
| Opac: | Controlla la disponibilità qui |