Titlebook: Advances in Intelligent Data Analysis XII; 12th International S Allan Tucker,Frank Höppner,Stephen Swift Conference proceedings 2013 Spring

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https://doi.org/10.1007/978-3-642-10745-0riments on time series forecasting show that including the constraints in the training phase particularly reduces the risk of overfitting in challenging situations with missing values or a large number of Gaussian components.

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Marie Chavent,Vanessa Kuentz,Jérôme Saraccoy performance is needed in order to know when AL works. Thus we also present a detailed methodology for tackling the complexities of assessing AL performance in the context of this experimental study.

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Almas Jabeen,Nadeem Ahmad,Khalid Razaccording to the induced variability within and between classes. The experiments performed on real and synthetic datasets demonstrate the ability of the multiple temporal matching approach to capture fine-grained distinctions between time series.

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0302-9743 he 12th International Conference on Intelligent Data Analysis, which was held in October 2013 in London, UK. The 36 revised full papers together with 3 invited papers were carefully reviewed and selected from 84 submissions handling all kinds of modeling and analysis methods, irrespective of discipl

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Ayşe Elif Canbilen,Murat Ceylanrcomes some of the drawbacks of existing methods. Our work consists in providing a simple and flexible framework to directly mine complex sequences of itemsets, by combining well-known properties on prefixes and suffixes. Experiments were performed on different real datasets to show the benefit of partially ordered patterns.

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0302-9743 ine. The papers cover all aspects of intelligent data analysis, including papers on intelligent support for modeling and analyzing data from complex, dynamical systems.978-3-642-41397-1978-3-642-41398-8Series ISSN 0302-9743 Series E-ISSN 1611-3349

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