SVM相关程序源码.zip_LibSVM_svmc_svm源码

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SVM相关程序源码.zip （28个子文件）

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Software

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SVM-Light Support Vector Machine.htm 31KB

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<!DOCTYPE HTML PUBLIC "-//W3C//DTD HTML 4.0 Transitional//EN">  <HTML><HEAD><TITLE>SVM-Light Support Vector Machine</TITLE> <META content="text/html; charset=windows-1252" http-equiv=Content-Type> <META content="MSHTML 5.00.2920.0" name=GENERATOR> <META content=8.0.3514 name=Version> <META content=11/26/96 name=Date> <META content="C:\Programme\Microsoft Office\Office\HTML.DOT" name=Template></HEAD> <BODY bgColor=#ffffff link=#0000ff vLink=#800080> <TABLE border=0 cellPadding=5 cellSpacing=0> <TBODY> <TR> <TD vAlign=top width="14%"> <H2><A href="http://www-ai.cs.uni-dortmund.de/logo.html"><IMG border=0 height=81 src="SVM-Light Support Vector Machine.files/eier_graybg.gif" width=100></A></H2></TD> <TD vAlign=top width="75%"> <H1 align=center>SVMlight </H1> <H1 align=center>Support Vector Machine</H1> Author: <A href="http://ais.gmd.de/~thorsten">Thorsten Joachims</A> <<A href="mailto:[email protected]">mailto:[email protected]</A>> <A href="http://www.gmd.de/">GMD Forschungszentrum Informationstechnik</A> <A href="http://ais.gmd.de/">Institute for Autonomous Intelligent Systems</A> <A href="http://ais.gmd.de/KD">Team Knowledge Discovery</A> Developed at: <A href="http://www.uni-dortmund.de/">University of Dortmund</A>, <A href="http://www.informatik.uni-dortmund.de/">Informatik</A>, <A href="http://www-ai.informatik.uni-dortmund.de/">AI-Unit</A> <A href="http://www.statistik.uni-dortmund.de/sfb475/sfb475.htm">Collaborative Research Center on 'Complexity Reduction in Multivariate Data' (SFB475)</A> Version: 3.50 Date: 09.11.00</TD> <TD vAlign=top width="11%"> <H2><IMG height=80 src="SVM-Light Support Vector Machine.files/gmd-m.gif" width=80></H2></TD></TR></TBODY></TABLE> <H2>Overview</H2> SVMlight is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the following: <UL> <LI>fast optimization algorithm <UL> <LI>working set selection based on steepest feasible descent <LI>"shrinking" heuristic <LI>caching of kernel evaluations <LI>use of folding in the linear case </LI></UL> <LI>computes XiAlpha-estimates of the error rate, the precision, and the recall <LI>efficiently computes Leave-One-Out estimates of the error rate, the precision, and the recall <LI>includes algorithm for approximately training large transductive SVMs (TSVMs) <LI>can train SVMs with cost models <LI>handles many thousands of support vectors <LI>handles several ten-thousands of training examples <LI>supports standard kernel functions and lets you define your own <LI>uses sparse vector representation </LI></UL> There is also a regression support vector machine based on SVMlight available at the AI-Unit: <A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/MYSVM">mySVM</A>. <H2>Description</H2> SVMlight is an implementation of Vapnik's Support Vector Machine [<A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html#References">Vapnik, 1995</A>] for the problem of pattern recognition. The optimization algorithm used in SVMlight is described in [<A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html#References">Joachims, 1999a</A>]. The algorithm has scalable memory requirements and can handle problems with many thousands of support vectors efficiently. This new version also provides methods for assessing the generalization performance efficiently. It includes two efficient estimation methods for both error rate and precision/recall. XiAlpha-estimates [<A href="http://ais.gmd.de/~thorsten/svm_light/#References">Joachims, 2000a</A>, <A href="http://ais.gmd.de/~thorsten/svm_light/#References">Joachims, 2000b</A>] can be computed at essentially no computational expense, but they are conservatively biased. Almost unbiased estimates provides leave-one-out testing. SVMlight exploits that the results of most leave-one-outs (often more than 99%) are predetermined and need not be computed [<A href="http://ais.gmd.de/~thorsten/svm_light/#References">Joachims, 2000b</A>]. Futhermore, this version includes an algorithm for training large-scale transductive SVMs. The algorithm proceeds by solving a sequence of optimization problems lower-bounding the solution using a form of local search. A detailed description of the algorithm can be found in [<A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html#References">Joachims, 1999c</A>]. SVMlight can also train SVMs with cost models (see [<A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html#References">Morik et al., 1999</A>]). The code has been used on a large range of problems, including text classification [<A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html#References">Joachims, 1999c</A>][<A href="http://www-ai.cs.uni-dortmund.de/SOFTWARE/SVM_LIGHT/svm_light.eng.html#References">Joachims, 1998a</A>], several image recognition tasks, and medical applications. Many tasks have the property of sparse instance vectors. This implementation makes use of this property which leads to a very compact and efficient representation. <H2>Source Code</H2> The source code is free for scientific use. Please contact me, if you are planning to use the software for commercial purposes. The software must not be modified and distributed without prior permission of the author. If you use SVMlight in your scientific work, please cite as <UL> <LI>T. Joachims, Making large-Scale SVM Learning Practical. Advances in Kernel Methods - Support Vector Learning, B. Sch�lkopf and C. Burges and A. Smola (ed.), MIT-Press, 1999. <A href="http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.pdf">[PDF]</A><A href="http://www-ai.cs.uni-dortmund.de/DOKUMENTE/joachims_99a.ps.gz">[Postscript (gz)]</A></LI></UL> I would also appreciate, if you sent me (a link to) your papers so that I can learn about your research. The implementation was developed on Solaris 2.5 with gcc, but compiles also on SunOS 3.1.4, Solaris 2.7, Linux, IRIX, Windows NT, and Powermac (after small modifications, see <A href="http://ais.gmd.de/~thorsten/svm_light/svm_light_faq.html">FAQ</A>). The source code is available at the following location: <DIR> <A href="ftp://ftp-ai.cs.uni-dortmund.de/pub/Users/t

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