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By Matthias Studer, Gilbert Ritschard, Alexis Gabadinho, Nicolas S. Müller (auth.), Fabrice Guillet, Gilbert Ritschard, Djamel Abdelkader Zighed, Henri Briand (eds.)

ISBN-10: 3642005799

ISBN-13: 9783642005794

ISBN-10: 3642005802

ISBN-13: 9783642005800

During the decade, the French-speaking medical group constructed a truly robust study task within the box of data Discovery and administration (KDM or EGC for “Extraction et Gestion des Connaissances” in French), that is interested by, between others, information Mining, wisdom Discovery, company Intelligence, wisdom Engineering and SemanticWeb. the new and novel study contributions amassed during this publication are prolonged and transformed types of a variety of the easiest papers that have been initially offered in French on the EGC 2009 convention held in Strasbourg, France on January 2009. the amount is prepared in 4 elements. half I contains 5 papers involved by means of quite a few points of supervised studying or info retrieval. half II provides 5 papers fascinated by unsupervised studying concerns. half III comprises papers on info streaming and on defense whereas partially IV the final 4 papers are interested by ontologies and semantic.

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Thus substituting for z from the constraint in terms of w and b into the objective function ψ of the quadratic programming (1) yields an unconstrained problem (2): min Ψ (w, b) = (1/2) w, b w, b 2 + (c/2) e − D(Aw − eb) 2 (2) In the optimal configuration for (2), the gradient with respect to w and b should be zero. This yields the linear equation system of (n + 1) variables (w1 , w2 , . . , wn , b) as follows: 1 (w1 , w2 , . . , wn , b)T = ( I + E T E)−1 E T De c (3) where E = [A − e], I denotes the identity matrix.

Section 5 reports comparative evaluations of the method. Finally, section 6 concludes the article. 24 N. Voisine, M. Boullé, and C. Hue 2 The MODL Approach For the convenience of the reader, this Section summarizes the MODL approach in the case of supervised discretization of numerical variables (Boullé, 2006). The objective of supervised discretization is to induce a list of intervals which partitions the numerical domain of a continuous input variable, while keeping the information relative to the output variable.

Our proposal is thus an hybridization of decision trees with SVMs. SVMs are here used in the growing phase to create the oblique trees. , 2007). Recently Simon et al. (2009) proposed to embedded multiple proximal SVM into a binary tree architecture for multi-classes problem. -N. Do et al. 4 GHz Pentium IV, 512 MB RAM)). Fig. 2 Linear separation of the datapoints into two classes Briefly, consider the linear binary classification task depicted in figure 2, with m datapoints xi (i = 1, m) in n dimensions (attributes).

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Advances in Knowledge Discovery and Management by Matthias Studer, Gilbert Ritschard, Alexis Gabadinho, Nicolas S. Müller (auth.), Fabrice Guillet, Gilbert Ritschard, Djamel Abdelkader Zighed, Henri Briand (eds.)


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