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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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書目名稱Emerging Paradigms in Machine Learning
編輯Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett
視頻videohttp://file.papertrans.cn/309/308330/308330.mp4
概述State of the art of emerging paradigms in machine learning including some real world applications.Latest research in machine learning and biologically-based techniques for the design and implementatio
叢書名稱Smart Innovation, Systems and Technologies
圖書封面Titlebook: Emerging Paradigms in Machine Learning;  Sheela Ramanna,Lakhmi C Jain,Robert J. Howlett Book 2013 Springer-Verlag Berlin Heidelberg 2013 Em
描述.This? book presents fundamental topics and algorithms that form the core of machine learning (ML) research, as well as emerging paradigms in intelligent system design. The ?multidisciplinary nature of machine learning makes it a very fascinating and popular area for research.? The book is aiming at students, practitioners and researchers and captures the diversity and richness of the field of machine learning and intelligent systems.? Several chapters are devoted to computational learning models such as granular computing, rough sets and fuzzy sets An account of applications of well-known learning methods in biometrics, computational stylistics, multi-agent systems, spam classification including an extremely well-written survey on Bayesian networks shed light on the strengths and weaknesses of the methods. Practical studies yielding insight into challenging problems such as learning from incomplete and imbalanced data, pattern recognition of stochastic episodic events and on-line mining of non-stationary data streams are a key part of this book.? ?.
出版日期Book 2013
關(guān)鍵詞Emerging paradigms; Emerging paradigms; Intelligent systems; Intelligent systems; Machine learning; Machi
版次1
doihttps://doi.org/10.1007/978-3-642-28699-5
isbn_softcover978-3-642-43574-4
isbn_ebook978-3-642-28699-5Series ISSN 2190-3018 Series E-ISSN 2190-3026
issn_series 2190-3018
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

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Emerging Trends in Machine Learning: Classification of Stochastically Episodic Eventss open for analysis. In particular, we note that this new realm deviates from the standard set of OC problems based on the presence of three characteristics, which ultimately amplify the classification challenge. They involve the . nature of the appearance of the data, the fact that the data from th
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Overlapping, Rare Examples and Class Decomposition in Learning Classifiers from Imbalanced Datalties. These results confirm the initial hypothesis saying the degradation of classification performance is more related to the minority class decomposition into small sub-parts. Another critical factor concerns presence of a relatively large number of borderline examples from the minority class in
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Needs Assessment in Public Health totally data driven in that it does not make any assumptions about the number or the maximum size of hyperboxes. Subsequent optimisation involves identification of granular prototypes and their refinement so as to achieve full reconstruction of the original data from the prototypes and the correspo
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https://doi.org/10.1007/978-3-663-12254-8lties. These results confirm the initial hypothesis saying the degradation of classification performance is more related to the minority class decomposition into small sub-parts. Another critical factor concerns presence of a relatively large number of borderline examples from the minority class in
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