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Titlebook: Computational Intelligence in Intelligent Data Analysis; Christian Moewes,Andreas Nürnberger Conference proceedings 2013 Springer-Verlag B

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書目名稱Computational Intelligence in Intelligent Data Analysis
編輯Christian Moewes,Andreas Nürnberger
視頻videohttp://file.papertrans.cn/233/232512/232512.mp4
概述Present sleading Research on Computational Intelligence in Intelligent Data Analysis.Festschrift published on the occasion of Rudolf Kruses 60th birthday.Written by experts in the field
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Computational Intelligence in Intelligent Data Analysis;  Christian Moewes,Andreas Nürnberger Conference proceedings 2013 Springer-Verlag B
描述.Complex systems and their phenomena are ubiquitous as they can be.found in biology, finance, the humanities, management sciences,.medicine, physics and similar fields..For many problems in these fields, there are no conventional ways to.mathematically or analytically solve them completely at low cost. On.the other hand, nature already solved many optimization problems.efficiently. Computational intelligence attempts to mimic.nature-inspired problem-solving strategies and methods..These strategies can be used to study, model and analyze complex.systems such that it becomes feasible to handle them. Key areas of.computational intelligence are artificial neural networks,.evolutionary computation and fuzzy systems..As only a few researchers in that field, Rudolf Kruse has contributed.in many important ways to the understanding, modeling and application.of computational intelligence methods. On occasion of his 60th.birthday, a collection of original papers of leading researchers in.the field of computational intelligence has been collected in this.volume. .
出版日期Conference proceedings 2013
關(guān)鍵詞Computational Intelligence; Intelligent Data Analysis
版次1
doihttps://doi.org/10.1007/978-3-642-32378-2
isbn_softcover978-3-642-43085-5
isbn_ebook978-3-642-32378-2Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2013
The information of publication is updating

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Can Fuzzy Clustering Avoid Local Minima and Undesired Partitions?ng, i.e. fuzzy clustering often tends to converge to the same clustering result independent of the initialisation whereas the result for crisp clustering is highly dependent on the initialisation. This leads to the conjecture that the objective function used for fuzzy clustering has less undesired l
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Kernel Based Defuzzification(MOM). A popular parametric defuzzification method is based on the basic defuzzification distribution (BADD). COG and MOM are special cases of BADD for unit and infinite exponents, respectively. Kernelization is a popular approach to improve data processing methods by implicit transformation to high
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Neuro-fuzzy Systems: A Short Historical Reviewpporting the development process by an automatic learning process. Just a few years earlier the backpropagation learning rule for multi-layer neural networks had been rediscovered and triggered a massive new interest in neural networks. The approach of combining fuzzy systems with neural networks in
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Safe and Interpretable Machine Learning: A?Methodological Reviewn and control require that the correctness of the model must be ensured not only for the available data but for all possible input combinations. Thus, understanding what the model has learned and in particular how it will extrapolate to unseen data is a crucial concern. The paper discusses suitable
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