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Date:      Wed, 19 Dec 2001 10:27:13 +0800
From:      Chia-Hsing Yu <davidyu@ob.m6.ntu.edu.tw>
To:        FreeBSD-gnats-submit@freebsd.org
Subject:   ports/32997: new ports for libsvm
Message-ID:  <E16GWRt-00026t-00@ob.m6.ntu.edu.tw>

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>Number:         32997
>Category:       ports
>Synopsis:       new ports for libsvm
>Confidential:   no
>Severity:       non-critical
>Priority:       low
>Responsible:    freebsd-ports
>State:          open
>Quarter:        
>Keywords:       
>Date-Required:
>Class:          update
>Submitter-Id:   current-users
>Arrival-Date:   Tue Dec 18 18:30:00 PST 2001
>Closed-Date:
>Last-Modified:
>Originator:     Chia-Hsing Yu
>Release:        FreeBSD 4.4-STABLE i386
>Organization:
NTU CSIE
>Environment:
System: FreeBSD ob.m6.ntu.edu.tw 4.4-STABLE FreeBSD 4.4-STABLE #0: Mon Dec 17 11:23:27 CST 2001 root@ob.m6.ntu.edu.tw:/usr/src/sys/compile/OB i386
>Description:
LIBSVM is an integrated software for support vector classification, (C-SVC,
nu-SVC ), regression (epsilon-SVR, nu-SVR) and distribution estimation
(one-class SVM ). It supports multi-class classification. The basic algorithm
is a simplification of both SMO by Platt and SVMLight by Joachims. It is also
a simplification of the modification 2 of SMO by Keerthi et al.

Our goal is to help users from other fields to easily use SVM as a tool.
LIBSVM provides a simple interface where users can easily link it with their
own programs. Main features of LIBSVM include

Different SVM formulations
Efficient multi-class classification
Cross validation for model selection
Weighted SVM for unbalanced data
Both C++ and Java sources
GUI demonstrating SVM classification and regression

http://www.csie.ntu.edu.tw/~cjlin/libsvm/

Author: Chih-Chung Chang and Chih-Jen Lin <cjlin@csie.ntu.edu.tw>
>How-To-Repeat:
>Fix:
	please fetch http://www.oio.cx/~davidyu/libsvm.tgz
>Release-Note:
>Audit-Trail:
>Unformatted:

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