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

By Claude Sammut, Geoffrey I. Webb

This finished encyclopedia, with over 250 entries in an A-Z layout, presents easy accessibility to suitable details for these looking access into any element in the vast box of laptop studying. such a lot entries during this preeminent paintings comprise necessary literature references.

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

The sort of the entries within the Encyclopedia of laptop Learning is expository and educational, making the ebook a pragmatic source for computing device studying specialists, in addition to execs in different fields who have to entry this very important info yet would possibly not have the time to paintings their manner via a complete textual content on their subject of interest.

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

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Atten Percept Psychophys 73:1147–1170 Grossberg S, Kuhlmann L, Mingolla E (2007) A neural model of 3D shape-from-texture: multiple-scale filtering, boundary grouping, and surface filling-in. Vis Res 47:634–672 Grossberg S, Mingolla E, Viswanathan L (2001) Neural dynamics of motion integration and segmentation within and across apertures. Vis Res 41:2521– 2553 Grossberg S, Srihasam K, Bullock D (2012) Neural dynamics of saccadic and smooth pursuit eye movement coordination during visual tracking of unpredictably moving targets.

Machine Learning Data Mining Statistics Information Theory ... Problem Characteristics Anomaly Detection Methods Problem Formulations Anomaly Detection, Fig. 1 Anatomy of an anomaly detection problem – – – – – Nature of input data Availability of labels Type of anomalies Expected output ... A 44 Anomaly Detection a b c o1 o1 o1 d e o2 f o1 o1 o1 o3 o4 Anomaly Detection, Fig. 2 Examples of anomalies in 2-D data. (a) Anomaly with respect to rest of the data points. (b) Anomaly with respect to local neighborhood.

ART networks for supervised learning are called ARTMAP (Carpenter et al. 1991a, 1992). A mismatch between an active top-down expectation and the bottom-up input leads to a parallel memory search (Fig. 1c). The ART matching criterion is set by a vigilance parameter . Low vigilance permits the learning of broad classes, across diverse exemplars, while high vigilance limits learning to narrow classes. When a new input arrives, vigilance equals a baseline level. Baseline vigilance is set equal to zero to maximize generalization.

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