[Comp-neuro] new SVM software available

Klaus Obermayer oby at cs.tu-berlin.de
Tue May 16 18:49:34 CEST 2006

Dear All,

I would like to announce a fast SMO implementation of the P-SVM, a new
SVM method for dyadic data.

The software is available for downloading via the web-address:


using the link "PSVM".

The P-SVM is described in detail in the recent publication:

S. Hochreiter and K. Obermayer, Support Vector Machines for Dyadic Data,
Neural Computation 18, 1472-1510.

An abstract is attached.

All the best



Support Vector Machines for Dyadic Data

S. Hochreiter & K. Obermayer

We describe a new technique for the analysis of dyadic data, where two
sets of objects ("row" and "column" objects) are characterized by a
matrix of numerical values which describe their mutual relationships.
The new technique, called "Potential Support Vector Machine" (P-SVM),
is a large-margin method for the construction of classifiers and 
regression functions for the "column" objects. Contrary to standard
support vector machine approaches, the P-SVM minimizes a scale-invariant
capacity measure and requires a new set of constraints. As a result, the
P-SVM method leads to a usually sparse expansion of the classification
and regression functions in terms of the "row" rather than the "column"
objects and can handle data and kernel matrices which are neither
positive definite nor square. We then describe two complementary
regularization schemes. The first scheme improves generalization
performance for classification and regression tasks, the second scheme
leads to the selection of a small, informative set of "row" "support"
objects and can be applied to feature selection. Benchmarks for
classification, regression, and feature selection tasks are performed
with toy data as well as with several real world data sets. The results
show, that the new method is at least competitive with but often
performs better than the benchmarked standard methods for standard
vectorial as well as for true dyadic data sets. In addition, a
theoretical justification is provided for the new approach.


Prof. Dr. Klaus Obermayer         phone:  49-30-314-73442
FR2-1, NI, Fakultaet IV                   49-30-314-73120
Technische Universitaet Berlin    fax:    49-30-314-73121
Franklinstrasse 28/29             e-mail: oby at cs.tu-berlin.de
10587 Berlin, Germany             http://ni.cs.tu-berlin.de/

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