Encyclopedia of Machine Learning by Claude Sammut, Geoffrey I. Webb

By Claude Sammut, Geoffrey I. Webb

This complete encyclopedia, with over 250 entries in an A-Z structure, presents quick access to correct details for these looking access into any element in the huge box of laptop studying. such a lot entries during this preeminent paintings comprise invaluable literature references.

Topics for the Encyclopedia of desktop Learning have been chosen by means of a amazing overseas advisory board. those peer-reviewed, highly-structured entries comprise definitions, illustrations, functions, bibliographies and hyperlinks to comparable literature, delivering the reader with a portal to extra unique info on any given topic.

The form of the entries within the Encyclopedia of laptop Learning is expository and instructional, making the booklet a realistic source for desktop studying specialists, in addition to execs in different fields who have to entry this very important details yet won't have the time to paintings their approach via a complete textual content on their subject of interest.

The authoritative reference is released either in print and on-line. The print ebook comprises an index of matters and authors. the web variation supplementations this index with links in addition to inner links to similar entries within the textual content, CrossRef citations, and hyperlinks to extra major research.

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Pool-Based Active Learning Pool-based active learning (McCallum & Nigam, ) is popular in domains such as text classification and speech recognition where unlabeled data are plentiful and cheap, but labels are expensive and slow to acquire. In pool-based active learning, the learner may not propose arbitrary points to label, but instead has access to a set of unlabeled examples, and is allowed to select which of them to request labels for. A special case of pool-based learning is transductive active learning, where the test distribution is exactly the set of unlabeled examples.

Journal of Applied Logic, (), –. , & Demetriades, I. (). Abductive logic programming in the clinical management of HIV/AIDS. In G. Brewka, S. Coradeschi, A. Perini, & P. ), Proceedings of the th European conference on artificial intelligence. Frontiers in artificial intelligence and applications (Vol. , pp. –). Amsterdam: IOS Press. , & Russo, A. (). Hybrid abductive inductive learning: A generalisation of Progol. In Proceedings of the th international conference on inductive logic programming.

Computational limitations on learning from examples. Journal of the ACM (JACM), (), –. Robbins, H. (). Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society, , –. , & Dietterich, T. (). What good are experiments? Proceedings of the sixth international workshop on machine learning. Ithaca, NY. Seung, H. , & Sompolinsky, H. (). Query by committee. In Proceedings of the fifth workshop on computational learning theory (pp.

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