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/libsvm-2.82/README

Libsvm is a simple, easy-to-use, and efficient software for SVM
classification and regression. It solves C-SVM classification, nu-SVM
classification, one-class-SVM, epsilon-SVM regression, and nu-SVM
regression. It also provides an automatic model selection tool for
C-SVM classification. This document explains the use of libsvm.

Libsvm is available at 
http://www.csie.ntu.edu.tw/~cjlin/libsvm
Ple
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/libsvm-2.82/python/README

Python-to-libsvm interface

Introduction
============

Python (http://www.python.org/) is a programming language suitable for
rapid development. This python-to-libsvm interface is developed so 
users can easily experiment with libsvm using python. The interface is 
developed with SWIG, 

The original idea and the SWIG interface file was provided by Carl Staelin
(staelin@hpl.hp.com) from HP Labs. T
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/libsvm-2.82/tools/README

This directory includes some useful codes:

1. subset selection tools.
2. parameter selection tools.

Part I: Subset selection tools

Introduction
============

Training large data is time consuming. Sometimes one should work on a
smaller subset first. The python script subset.py randomly selects a
specified number of samples. For classification data, we provide a
stratified selection to ensure th
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/libsvm-2.82/FAQ.html

<html>
<head>
<title>LIBSVM FAQ</title>
</head>
<body bgcolor="#ffffcc">

<a name="_TOP"><b><h
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/libsvm-2.82/COPYRIGHT

Copyright (c) 2000-2005 Chih-Chung Chang and Chih-Jen Lin
All rights reserved.

Redistribution and 
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