Download Encyclopedia of Machine Learning by Claude Sammut, Geoffrey I. Webb PDF
By Claude Sammut, Geoffrey I. Webb
This accomplished encyclopedia, with over 250 entries in an A-Z layout, offers easy accessibility to appropriate details for these looking access into any element in the huge box of laptop studying. such a lot entries during this preeminent paintings comprise priceless literature references.
Topics for the Encyclopedia of computer Learning have been chosen by means of a exclusive foreign advisory board. those peer-reviewed, highly-structured entries contain definitions, illustrations, purposes, bibliographies and hyperlinks to comparable literature, delivering the reader with a portal to extra precise details on any given topic.
The variety of the entries within the Encyclopedia of desktop Learning is expository and instructional, making the booklet a pragmatic source for computer studying specialists, in addition to pros in different fields who have to entry this very important info yet would possibly not have the time to paintings their approach via a whole 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 version 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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Extra info for Encyclopedia of Machine Learning
Bryant, C. (). Theory completion using inverse entailment. In Proceedings of the tenth international workshop on inductive logic programming (ILP-) (pp. –). Berlin: Springer. , & Mooney, R. J. (). Theory refinement combining analytical and empirical methods. Artificial Intelligence, , –. , & Sergot, M. (). Inference of gene relations from microarray data by abduction. In Proceedings of the eighth international conference on logic programming and non-monotonic reasoning (LPNMR’) (Vol.
Motivation and Background Abduction is, along with induction, a synthetic form of reasoning whereby it generates, in its explanations, new information not hitherto contained in the current theory with which the reasoning is performed. As such, it has a natural relation to learning, and in particular to knowledge intensive learning, where the new information generated aims to complete, at least partially, the current knowledge (or model) of the problem domain as described in the given theory. Early uses of abduction in the context of machine learning concentrated on how abduction can be used as a theory revision operator for identifying where the current theory could be revised in order to accommodate the new learning data.
Loss minimization (Cohn, Ghahramani, & Jordan, ). Uncertainty sampling can stumble when parts of the learner’s domain are inherently noisy. It may be that, regardless of the number of samples labeled in some neighborhood, it will remain impossible to accurately predict these. In these cases, it would be desirable to not only model the learner’s uncertainty over arbitrary parts of its domain, but also to model what effect labeling any future example is expected to have on that uncertainty. , for locally-weighted regression and mixture models, these estimates may be computed in closed form).