本章目录

14 Kernels 479
14.1 Introduction 479
14.2 Kernel functions 479
14.2.1 RBF kernels 480
14.2.2 Kernels for comparing documents 480
14.2.3 Mercer (positive definite) kernels 481
14.2.4 Linear kernels 482
14.2.5 Matern kernels 482
14.2.6 String kernels 483
14.2.7 Pyramid match kernels 484
14.2.8 Kernels derived from probabilistic generative models 485
14.3 Using kernels inside GLMs 486
14.3.1 Kernel machines 486
14.3.2 L1VMs, RVMs, and other sparse vector machines 487
14.4 The kernel trick 488
14.4.1 Kernelized nearest neighbor classification 489
14.4.2 Kernelized K-medoids clustering 489
14.4.3 Kernelized ridge regression 492
14.4.4 Kernel PCA 493
14.5 Support vector machines (SVMs) 496
14.5.1 SVMs for regression 497
14.5.2 SVMs for classification 498
14.5.3 Choosing C 504
14.5.4 Summary of key points 504
14.5.5 A probabilistic interpretation of SVMs 505
14.6 Comparison of discriminative kernel methods 505
14.7 Kernels for building generative models 507
14.7.1 Smoothing kernels 507
14.7.2 Kernel density estimation (KDE) 508
14.7.3 From KDE to KNN 509
14.7.4 Kernel regression 510
14.7.5 Locally weighted regression 512

github下载链接：https://github.com/916718212/Machine-Learning-A-Probabilistic-Perspective-.git