Titlebook: New Frontiers in Mining Complex Patterns; 6th International Wo Annalisa Appice,Corrado Loglisci,Zbigniew W. Ras Conference proceedings 2018

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New Frontiers in Mining Complex Patterns978-3-319-78680-3Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/n/image/665287.jpg

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Learning Association Rules for Pharmacogenomic Studies,metrix DMET (Drug Metabolizing Enzymes and Transporters) microarray platform offers the possibility to determine the gene variants of a patient and correlate them with drug-dependent adverse events. The analysis of DMET data is a growing research area. Existing approaches span from the use of simple

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Segment-Removal Based Stuttered Speech Remediation,rtions of speech which can be deleted without diminishing from the speech quality, but rather improving the speech. Speech remediation is especially important when the speech is disfluent as in the case of stuttered speech. In this paper, we describe a stuttered speech remediation approach based on

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Density Estimators for Positive-Unlabeled Learning,g requires algorithms to cleverly exploit dependencies hidden in the unlabeled data in order to build models able to accurately discriminate between positive and negative samples. We propose to exploit probabilistic generative models to characterize the distribution of the positive samples, and to l