| 书目名称 | Kernel Based Algorithms for Mining Huge Data Sets |
| 副标题 | Supervised, Semi-sup |
| 编辑 | Te-Ming Huang,Vojislav Kecman,Ivica Kopriva |
| 视频video | |
| 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: |
| 丛书名称 | Studies in Computational Intelligence |
| 图书封面 |  |
| 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 |
| 关键词 | Analysis; MATLAB; Regression; Signal; algorithm; algorithms; bioinformatics; classification; learning; machin |
| 版次 | 1 |
| doi | https://doi.org/10.1007/3-540-31689-2 |
| isbn_softcover | 978-3-642-06856-0 |
| isbn_ebook | 978-3-540-31689-3Series ISSN 1860-949X Series E-ISSN 1860-9503 |
| issn_series | 1860-949X |
| copyright | Springer-Verlag Berlin Heidelberg 2006 |
发表于 2025-3-21 18:18:24
