Software

LS-SVMlab:
Least Squares - Support Vector Machines Matlab/C Toolbox
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

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.