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In closing, Part IV addresses the development of evolutionary learning algorithms with provable theoretical guarantees for several representative tasks, in which evolutionary learning offers excellent performance.
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Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning.
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Ensemble methods that train multiple learners and then combine them to use, with \textit{Boosting} and \textit{Bagging} as representatives, are well-known... Læs mere