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.
Since version 2.8, it implements an SMO-type algorithm proposed in this paper:
R.-E. Fan, P.-H. Chen, and C.-J. Lin. Working set selection using second order
information for training SVM. Journal of Machine Learning Research 6,
1889-1918, 2005. You can also find a pseudo code there.
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
* Probability estimates
* Weighted SVM for unbalanced data
* Both C++ and Java sources
* GUI demonstrating SVM classification and regression
* Python, R (also Splus), MATLAB, Perl, Ruby, Weka, Common LISP and LabVIEW
interfaces. C# .NET code is available.
It's also included in some learning environments: YALE and PCP.
* Automatic model selection which can generate contour of cross valiation
Author: Chih-Chung Chang and Chih-Jen Lin <email@example.com>
To install the port:cd /usr/ports/science/libsvm-python/ && make install clean To add the package:pkg install science/libsvm-python
===> The following configuration options are available for libsvm-python-3.18:
DOCS=on: Build and/or install documentation
OPTIMIZED_CFLAGS=on: Use extra compiler optimizations
===> Use 'make config' to modify these settings
The vast majority of pkg-descr files had the following format when they
had both lines:
So standardize on that, and move them to the end of the file when necessary.
Also fix some more whitespace, and remove more "signature tags" of varying
forms, like -- name, etc.
A few other various formatting issues