We are glad to announce
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LS-SVMlab:
Least Squares - Support Vector Machines Matlab/C Toolbox
http://www.esat.kuleuven.ac.be/sista/lssvmlab/
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Toolbox:
. Matlab LS-SVMlab1.4 - Linux and Windows Matlab/C code
. Basic and advanced versions
. Functional and object oriented interface
Tutorial User's Guide (100pp.):
. Examples and demos
. Matlab functions with help
Solving and handling:
. Classification, Regression
. Tuning, cross-validation, fast loo,
receiver operating characteristic (ROC) curves
. Small and unbalanced data sets
. High dimensional input data
. Bayesian framework with three levels of inference
. Probabilistic interpretations, error bars
. hyperparameter selection, automatic relevance determination (ARD)
input selection, model comparison
. Multi-class encoding/decoding
. Sparseness
. Robustness, robust weighting, robust cross-validation
. Time series prediction
. Fixed size LS-SVM, Nystrom method,
kernel principal component analayis (kPCA), ridge regression
. Unsupervised learning
. Large scale problems
Related links, publications, presentations and book:
http://www.esat.kuleuven.ac.be/sista/lssvmlab/
Contact: LS-SVMlab@esat.kuleuven.ac.be
GNU General Public License:
The LS-SVMlab software is made available for research purposes only
under the GNU General Public License. LS-SVMlab software may not be
used for commercial purposes without explicit written permission after
contacting LS-SVMlab@esat.kuleuven.ac.be.
[we apologize for receiving multiple copies of this message]
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