Advances in Knowledge Discovery and Management - download pdf or read online

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 built a truly robust examine job within the box of information Discovery and administration (KDM or EGC for “Extraction et Gestion des Connaissances” in French), that's fascinated about, between others, info Mining, wisdom Discovery, enterprise Intelligence, wisdom Engineering and SemanticWeb. the new and novel study contributions accumulated during this publication are prolonged and transformed types of a range of the easiest papers that have been initially awarded in French on the EGC 2009 convention held in Strasbourg, France on January 2009. the quantity is geared up in 4 components. half I contains 5 papers involved by way of a number of elements of supervised studying or info retrieval. half II provides 5 papers inquisitive about unsupervised studying concerns. half III comprises papers on information streaming and on safeguard whereas partially IV the final 4 papers are fascinated about ontologies and semantic.

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MODL: a Bayes optimal discretization method for continuous attributes. : Compression-Based Averaging of Selective Naive Bayes Classifiers. Journal of Machine Learning Research 8, 1659–1685 (2007) 38 N. Voisine, M. Boullé, and C. : Classification And Regression Trees. : Simplifying decision trees: A survey. Knowl. Eng. Rev. : Elements of Information Theory. : ROC Graphs: Notes and Practical Considerations for Researchers. : WEKA: The Waikato Environment for Knowledge Analysis. In: Proc. of the New Zealand Computer Science Research Students Conference, pp.

A Bayes Evaluation Criterion for Decision Trees 25 Once the evaluation criterion is established, the problem is to design a search algorithm in order to find a discretization model that minimizes the criterion. In Boullé (2006), a standard greedy bottom-up heuristic is used to find a good discretization. In order to further improve the quality of the solution, the MODL algorithm performs post-optimizations based on hill-climbing search in the neighbourhood of a discretization. The neighbors of a discretization are defined with combinations of interval splits and interval merges.

Problems in the analysis of Survey data, And a proposal. : Automatic construction of decision trees from data: A multi-disciplinary survey. : NP-completeness of problems of construction of optimal decision trees. : Well-trained PETs: Improving Probability Estimation Trees. : The case against accuracy estimation for comparing induction algorithms. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. : Inferring decision trees using the minimum description length principle.

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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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