Combining a Filter Method with SVMs

Lal, TN; Chapelle, O; Schölkopf, B; Guyon,; I.,; Gunn, S.; Nikravesh, M.; Zadeh, L. A.

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Combining a Filter Method with SVMs

Lal, T., Chapelle, O., & Schölkopf, B. (2006). Combining a Filter Method with SVMs. In Feature Extraction: Foundations and Applications (pp. 439-446). Berlin, Germany: Springer.

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Datensatz-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D32D-E Versions-Permalink: https://hdl.handle.net/11858/00-001M-0000-0013-D32E-C

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Lal, TN¹, Autor
Chapelle, O¹, Autor
Schölkopf, B¹, Autor
Guyon, Herausgeber
I., Herausgeber
Gunn, S., Herausgeber
Nikravesh, M., Herausgeber
Zadeh, L. A., Herausgeber

Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795

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Zusammenfassung: Our goal for the competition (feature selection competition NIPS 2003) was to evaluate the usefulness of simple machine learning techniques. We decided to use the correlation criteria as a feature selection method and Support Vector Machines for the classification part. Here we explain how we chose the regularization parameter C of the SVM, how we determined the kernel parameter and how we estimated the number of features used for each data set. All analyzes were carried out on the training sets of the competition data. We choose the data set Arcene as an example to explain the approach step by step. In our view the point of this competition was the construction of a well performing classifier rather than the systematic analysis of a specific approach. This is why our search for the best classifier was only guided by the described methods and that we deviated from the road map at several occasions. All calculations were done with the software Spider [2004].