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Introduction Data mining Data mining is an interdisciplinary field that combines artificial intelligence, database management, data visualization, machine learning, mathematic algorithms, and statistics

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9 43 53 Gamberger, D. and Lavra, N. 1997. Conditions for Occam's razor applicability and noise elimination. In: Proc. 9th European Conf. Machine

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fMRI studies, the number of features is much more than the number of subjects, which leads to the “curse of dimensionality” in machine learning studies ( Fort & Lambert, 2005 ; Martino et al., 2007 ). Feature extraction allows neuroimaging data (ReHo

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CiteSeer data which we will briefly mention. Zhou et al. ( 2007 ) have investigated documents from CiteSeer to discover temporal social network communities in the domains of databases and machine learning. On the other hand, Hopcroft et al. ( 2004

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Academic Publishers Dordrecht, The Netherlands . Quinlan , JR 1993 C4.5: Programs for machine learning Morgan Kaufmann San Francisco, CA, USA

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work quickly often prefer to publish in conferences and workshops proceedings. Many computer science subareas have their own top conferences and journals. For example, in machine learning, the two conferences Intl. Conference on Machine

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international conference on Machine learning (pp. 113 – 120 ). New York : ACM . 5. Blei , D , Lafferty , J . A correlated topic model of science . The Annals of Applied Statistics 2007 1 1

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Eighteenth International Conference on Machine Learning (pp. 250 – 257 ). San Francisco, CA : Morgan Kaufmann Publishers Inc . Kolda , T. G. , & Bader , W. B. ( 2006 ). The TOPHITS model for higher

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Introduction In several scientific areas—like ecology, information retrieval, machine learning, psychology and scientometrics—the measurement of similarity between objects plays a role. In scientometrics, examples of considered

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Biologia Futura
Authors: Anna Gábor, Noémi Kaszás, Ádám Miklósi, Tamás Faragó, and Attila Andics

some documentation that dog barking contains enough information for IR. Using different types of machine-learning approaches, both Molnár et al. ( 2008 ) and Larrañaga et al. ( 2014 ) were able to recognize individual dogs well above the level of

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