KDnuggets : News : 2002 : n23 : item13 < PREVIOUS | NEXT >


From: Johan Suykens
Date: 29 Nov 2002
Subject: LS-SVMlab Toolbox announcement

Least Squares - Support Vector Machines Matlab/C 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.

KDnuggets : News : 2002 : n23 : item13 < PREVIOUS | NEXT >

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