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Titlebook: Emerging Paradigms in Machine Learning; Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett Book 2013 Springer-Verlag Berlin Heidelberg 2013 Em

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發(fā)表于 2025-3-30 11:22:32 | 只看該作者
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發(fā)表于 2025-3-31 00:10:10 | 只看該作者
Mining Incomplete Data—A Rough Set Approachoximations are computed first and then the rule induction algorithm is applied. In the local version of MLEM2, rule sets are induced directly from raw data sets, incomplete and with numerical attributes. Results of experiments on both approaches are included.
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發(fā)表于 2025-3-31 04:22:13 | 只看該作者
Roles Played by Bayesian Networks in Machine Learning: An Empirical Investigationi) learning and (iii) postprocessing (rule generation). By doing so the chapter contributes with organizing, specifying and discussing the many different ways Bayes-based concepts can successfully be employed in automatic learning.
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Support Vector Machines in Biomedical and Biometrical Applicationsists, bioinformaticians, and biostatisticians from the Faculty of Automatic Control, Electronics and Computer Science at Silesian University of Technology in a collaboration with clinicists from the Institute of Oncology in Gliwice, Poland.
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發(fā)表于 2025-3-31 20:39:11 | 只看該作者
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