LS-SVMlab announcement

[Johan Suykens]

We are glad to announce

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LS-SVMlab:
Least Squares - Support Vector Machines Matlab/C Toolbox
 
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Website: http://www.esat.kuleuven.ac.be/sista/lssvmlab/


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 at 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 at esat.kuleuven.ac.be.