Application of Machine Learning by Yagang Zhang PDF

By Yagang Zhang

ISBN-10: 9533070358

ISBN-13: 9789533070353

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Masulli, F. and Sperduti, A. (2002 ), “Theoretical and experimental analysis of a two-stage system for classification,” IEEE TPAMI, vol. 24, no. 7, pp. 893-904. & Cakmakov, D. (2004), "An efficient three-stage classifier for handwritten digit recognition", ICPR'04, pp. 1051-4651. Kononenko, I. (1994) “Estimating attributes: analysis and extensions of Relief,” ECML-94, pp. 171–182. Kaynak, C. & Alpaydin, E. (1997), “Multistage classification by cascaded classifiers,” Proceedings of 1997 IEEE international symposium on Intelligent Control, pp.

R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. , Spatscheck, O. & Duffield, N. (2004). Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification, IMC ’04: Proceedings of the 4th ACM SIGCOMM conference on Internet measurement, ACM, New York, NY, USA, pp. 135–148. Verticale, G. (2009). An empirical study of self-similarity in the per-user-connection arrival process, Telecommunications, 2009.

44 Application of Machine Learning Algorithm 1 // we will denote the pool of classifiers by the //unordered set {Sp}, // and the final stage by SF. // the classifiers of {Sp} have arbitrary numbering // Sc denotes the cascade selected so far Inputs A pool of classifiers {Sp} and a powerful classifier SF Outputs A cascade Sc. Initialize best_complexity = complexity([SF]). Sc=[]. //an empty array Begin while (there are classifiers left in {Sp}) { k  arg min{complexity ([S c iSi{S P } new _ complexity  complexity ([S c S k Si S F ])} S F ]).

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Application of Machine Learning by Yagang Zhang


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