本章目录

15 Gaussian processes 515
15.1 Introduction 515
15.2 GPs for regression 516
15.2.1 Predictions using noise-free observations 517
15.2.2 Predictions using noisy observations 518
15.2.3 Effect of the kernel parameters 519
15.2.4 Estimating the kernel parameters 521
15.2.5 Computational and numerical issues * 524
15.2.6 Semi-parametric GPs * 524
15.3 GPs meet GLMs 525
15.3.1 Binary classification 525
15.3.2 Multi-class classification 528
15.3.3 GPs for Poisson regression 531
15.4 Connection with other methods 532
15.4.1 Linear models compared to GPs 532
15.4.2 Linear smoothers compared to GPs 533
15.4.3 SVMs compared to GPs 534
15.4.4 L1VM and RVMs compared to GPs 534
15.4.5 Neural networks compared to GPs 535
15.4.6 Smoothing splines compared to GPs * 536
15.4.7 RKHS methods compared to GPs * 538
15.5 GP latent variable model 540
15.6 Approximation methods for large datasets 542

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