Download Multiple Classifier Systems: 4th International Workshop, MCS by Mohamed S. Kamel, Nayer M. Wanas (auth.), Terry Windeatt, PDF

By Mohamed S. Kamel, Nayer M. Wanas (auth.), Terry Windeatt, Fabio Roli (eds.)

This e-book constitutes the refereed complaints of the 4th foreign Workshop on a number of Classifier structures, MCS 2003, held in Guildford, united kingdom in June 2003.

The forty revised complete papers offered with one invited paper have been rigorously reviewed and chosen for presentation. The papers are geared up in topical sections on boosting, mix ideas, multi-class tools, fusion schemes and architectures, neural community ensembles, ensemble techniques, and applications

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Additional info for Multiple Classifier Systems: 4th International Workshop, MCS 2003 Guildford, UK, June 11–13, 2003 Proceedings

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L, so l i=1 D1 (i) = 1), and the first base learner is trained. Then, at each round, RA increases the weights of patterns having a higher error in the last classifier: Dt+1 (i) = (Dt (i) exp(−αt yi ot (xi ))) /Zt (2) l i=1 Dt+1 (i) = 1, and αt where Zt is a normalization factor that assures that is the weight RA gives to the t-th classifier αt = where 1 + rt 1 − rt (3) Dt (i)yi ot (xi ) . (4) 1 ln 2 l rt = i=1 Once all the classifiers have been set up, the overall output of the combined scheme is calculated as T y(x) = sign(o(x)) = sign αt ot (x) .

D = ∅, the ensemble of classifiers. · L, the number of classifiers to train. 2. For k = 1, . . , L · Take a sample Sk from Z using distribution wk . · Build a classifier Dk using Sk as the training set. · Calculate the weighted error of Dk by N k wjk lkj , = (1) j=1 lkj = 1 if Dk misclassifies zj and lkj = 0, otherwise. 5), 1− k · Update the individual weights j k 1 N . (2) ξ(l ) wjk+1 = wjk βk N i=1 ξ(li ) wik βk k , j = 1, . . , N. (3) where ξ(lkj ) is a function which specifies which of the Boosting variants we use.

3. Y. Censor and A. Lent. An iterative row-action method for interval convex programming. Journal of Optimization Theory and Applications, 34(3):321–353, 1981. 4. Thomas G. Dietterich. An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40:139–158, Aug. 2000. 5. Y. Freund and R. Schapire. Experiments with a new boosting algorithm. In Proceedings of the Thirteenth International Conference on Machine Learning, pages 148–156, Bari, Italy, 1996.

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