Synopsis
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.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition
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