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: To Unsubscribe: send mail to majordomo@FreeBSD.org with "unsubscribe freebsd-ports" in the body of the message
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