Download e-book for kindle: Advances in Knowledge Discovery and Management by Matthias Studer, Gilbert Ritschard, Alexis Gabadinho,

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 neighborhood constructed a truly robust examine task within the box of data Discovery and administration (KDM or EGC for “Extraction et Gestion des Connaissances” in French), that's eager about, between others, info Mining, wisdom Discovery, company Intelligence, wisdom Engineering and SemanticWeb. the hot and novel examine contributions accrued during this ebook are prolonged and remodeled types of a variety of the simplest papers that have been initially provided in French on the EGC 2009 convention held in Strasbourg, France on January 2009. the amount is geared up in 4 elements. half I comprises 5 papers involved through numerous features of supervised studying or info retrieval. half II provides 5 papers all in favour of unsupervised studying concerns. half III contains papers on facts streaming and on safety whereas partly IV the final 4 papers are focused on ontologies and semantic.

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As pointed out by Rokach and Maimon (2005) finding the best linear combination can be achieved in different ways. , 1984). With multivariate splitting criteria each test is equivalent to a hyperplane with an oblique orientation to the axes. , 1993, 1994). Indeed, the greedy approaches can deal only with low dimensional datasets due to combinatorial explosion. The OC1 approach was extended by Wu et al. (1999) by modifying the splitting criterion of the basic OC1 algorithm or by post-processing OC1 output.

The optimization is performed using top-down heuristics with pre-pruning and post-pruning processes. Extensive experiments on 30 UCI datasets and on the 5 WCCI 2006 performance prediction challenge datasets show that our method obtains predictive performance similar to that of alternative state-of-the-art methods, with far simpler trees. Keywords: Decision Tree, Bayesian Optimization, Minimum Description Length, Supervised Learning, Model Selection. 1 Introduction Building decision trees from training data is a problem which has begun to be treated in 1963 by Morgan and Sonquist.

Proceedings of the National Academy of Sciences of the United States of America 103(51), 19430–19435 (2006) A Bayes Evaluation Criterion for Decision Trees Nicolas Voisine, Marc Boullé, and Carine Hue Abstract. We present a new evaluation criterion for the induction of decision trees. We exploit a parameter-free Bayesian approach and propose an analytic formula for the evaluation of the posterior probability of a decision tree given the data. We thus transform the training problem into an optimization problem in the space of decision tree models, and search for the best tree, which is the maximum a posteriori (MAP) one.

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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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