内容

目次

Introduction.- Overview of supervised learning.- Linear methods for regression.- Linear methods for classification.- Basis expansions and regularization.- Kernel smoothing methods.- Model assessment and selection.- Model inference and averaging.- Additive models, trees, and related methods.- Boosting and additive trees.- Neural networks.- Support vector machines and flexible discriminants.- Prototype methods and nearest-neighbors.- Unsupervised learning.