Libsvm

lastmod 2018-08-17created 2014-10-2912 edits

LIBSVM - A Library for Support Vector Machines.

How to use

Training data required by libsvm is entered in the following form.

<label> <index1>:<value1> <index2>:<value2> ...

Here, label is the class number, and index simply increases by 1 starting from 0.
The value becomes the value of the feature.

License

https://github.com/cjlin1/libsvm/blob/master/COPYRIGHT

Compatible with the BSD license. The full text is as follows.

Copyright (c) 2000-2014 Chih-Chung Chang and Chih-Jen Lin
All rights reserved.

Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:

1. Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.

2. Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.

3. Neither name of copyright holders nor the names of its contributors
may be used to endorse or promote products derived from this software
without specific prior written permission.


THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE REGENTS OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

SVM Parameters

/**
 * probability estimates.
 *
 * @remarks
 *  whether to predict probability estimates, 0 or 1 (default 0). @n
 *  for one-class SVM only 0 is supported.
 * @warning
 *  Current not used.
 */
int _predict_probability;

/**
 * shrinking heuristics flag.
 *
 * @remarks
 *  whether to use the shrinking heuristics, 0 or 1 (default 1)
 */
int _shrinking_heuristics;

/**
 * n-fold cross validation mode.
 *
 * @remarks
 *   0 or 1 (default 0).
 * @warning
 *  Current not used.
 */
int _cross_validation;

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References

Libsvm_-class_classifier_02-_training_and_test.pdf ↩

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