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Titlebook: Minimum Error Entropy Classification; Joaquim P. Marques de Sá,Luís M.A. Silva,Luís A. A Book 2013 Springer-Verlag Berlin Heidelberg 2013

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書目名稱Minimum Error Entropy Classification
編輯Joaquim P. Marques de Sá,Luís M.A. Silva,Luís A. A
視頻videohttp://file.papertrans.cn/635/634630/634630.mp4
概述Presents data classification methodologies based on a minimum error entropy approach.Includes both theoretical results and applications to real world datasets.Written by leading experts in the field
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Minimum Error Entropy Classification;  Joaquim P. Marques de Sá,Luís M.A. Silva,Luís A. A Book 2013 Springer-Verlag Berlin Heidelberg 2013
描述.This book explains the minimum error entropy (MEE) concept applied to data classification machines. Theoretical results on the inner workings of the MEE concept, in its application to solving a variety of classification problems, are presented in the wider realm of risk functionals..Researchers and practitioners also find in the book a detailed presentation of practical data classifiers using MEE. These include multi‐layer perceptrons, recurrent neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions..
出版日期Book 2013
關(guān)鍵詞Computational Intelligence; Information Theoretic Learning; Minimum Error Entropy Classification
版次1
doihttps://doi.org/10.1007/978-3-642-29029-9
isbn_softcover978-3-642-43742-7
isbn_ebook978-3-642-29029-9Series 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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Book 2013 neural networks, complexvalued neural networks, modular neural networks, and decision trees. A clustering algorithm using a MEE‐like concept is also presented. Examples, tests, evaluation experiments and comparison with similar machines using classic approaches, complement the descriptions..
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Joaquim P. Marques de Sá,Luís M.A. Silva,Luís A. APresents data classification methodologies based on a minimum error entropy approach.Includes both theoretical results and applications to real world datasets.Written by leading experts in the field
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A Scale-Aware YOLO Model for Pedestrian Detectionization within a scene. Recently, convolutional neural networks (CNNs) have been demonstrated to achieve superior detection results compared to traditional approaches. Although YOLOv3 (an improved You Only Look Once model) is proposed as one of state-of-the-art methods in CNN-based object detection,
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