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Titlebook: Kernel Based Algorithms for Mining Huge Data Sets; Supervised, Semi-sup Te-Ming Huang,Vojislav Kecman,Ivica Kopriva Book 2006 Springer-Verl

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發(fā)表于 2025-3-21 18:18:24 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Kernel Based Algorithms for Mining Huge Data Sets
副標題Supervised, Semi-sup
編輯Te-Ming Huang,Vojislav Kecman,Ivica Kopriva
視頻videohttp://file.papertrans.cn/543/542447/542447.mp4
概述Reports recent research results on Kernel Based Algorithms for Mining Huge Data Sets.A book about (machine) learning from (experimental) data.Includes supplementary material:
叢書名稱Studies in Computational Intelligence
圖書封面Titlebook: Kernel Based Algorithms for Mining Huge Data Sets; Supervised, Semi-sup Te-Ming Huang,Vojislav Kecman,Ivica Kopriva Book 2006 Springer-Verl
描述."Kernel Based Algorithms for Mining Huge Data Sets" is the first book treating the fields of supervised, semi-supervised and unsupervised machine learning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differences between the two most popular unsupervised techniques, the principal component analysis (PCA) and the independent component analysis (ICA). The book presents various examples, software, algorithmic solutions enabling the reader to develop their own codes for solving the problems. The book is accompanied by a website for downloading both data and software for huge data sets modeling in a supervised and semisupervised manner, as well as MATLAB based PCA and ICA routines for unsupervised learning. The book focuses on a broad range of machine learning algorithms and it is particularly aimed at students, scientists, and practicing researchers in bioinformatics (gene microarrays), text-categorization, numerals recognition, as well as in the im
出版日期Book 2006
關鍵詞Analysis; MATLAB; Regression; Signal; algorithm; algorithms; bioinformatics; classification; learning; machin
版次1
doihttps://doi.org/10.1007/3-540-31689-2
isbn_softcover978-3-642-06856-0
isbn_ebook978-3-540-31689-3Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2006
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:33:43 | 只看該作者
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,Support Vector Machines in Classification and Regression — An Introduction,
地板
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Iterative Single Data Algorithm for Kernel Machines from Huge Data Sets: Theory and Performance,
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Unsupervised Learning by Principal and Independent Component Analysis,
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Book 2006rning collectively. The book presents both the theory and the algorithms for mining huge data sets by using support vector machines (SVMs) in an iterative way. It demonstrates how kernel based SVMs can be used for dimensionality reduction (feature elimination) and shows the similarities and differen
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發(fā)表于 2025-3-23 04:14:30 | 只看該作者
Te-Ming Huang,Vojislav Kecman,Ivica KoprivaReports recent research results on Kernel Based Algorithms for Mining Huge Data Sets.A book about (machine) learning from (experimental) data.Includes supplementary material:
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發(fā)表于 2025-3-23 09:27:51 | 只看該作者
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