SVM-Light Support Vector Machine
http://svmlight.joachims.org/[2009-4-14 11:34:22]
SVM
light
Support Vector Machine
Author: Thorsten Joachims <thorsten@joachims.org>
Cornell University
Department of Computer Science
Developed at:
University of Dortmund, Informatik, AI-Unit
Collaborative Research Center on 'Complexity Reduction in Multivariate Data'
(SFB475)
Version: 6.02
Date: 14.08.2008
Overview
SVM
light
is an implementation of Support Vector Machines (SVMs) in C. The main features of the program are the
following:
fast optimization algorithm
working set selection based on steepest feasible descent
"shrinking" heuristic
caching of kernel evaluations
use of folding in the linear case
solves classification and regression problems. For multivariate and structured outputs use SVM
struct
.
solves ranking problems (e. g. learning retrieval functions in STRIVER search engine).
computes XiAlpha-estimates of the error rate, the precision, and the recall
efficiently computes Leave-One-Out estimates of the error rate, the precision, and the recall
includes algorithm for approximately training large transductive SVMs (TSVMs) (see also Spectral Graph
Transducer)
can train SVMs with cost models and example dependent costs
allows restarts from specified vector of dual variables
handles many thousands of support vectors
handles several hundred-thousands of training examples
supports standard kernel functions and lets you define your own
uses sparse vector representation
SVM
struct
: SVM learning for multivariate and structured outputs like trees, sequences, and sets (available here).
SVM
perf
: New training algorithm for linear classification SVMs that can be much faster than SVM
light
for large
datasets. It also lets you directly optimize multivariate performance measures like F1-Score, ROC-Area, and the
Precision/Recall Break-Even Point. (available here).
SVM
rank
: New algorithm for training Ranking SVMs that is much faster than SVM
light
in '-z p' mode. (available
here).
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