Feature construction and parameter setting for Support Vector Machines

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Authors

KUBA Petr

Year of publication 2003
Type Article in Proceedings
Conference Proceedings of the 2nd Conference Znalosti 2003
MU Faculty or unit

Faculty of Informatics

Citation
Field Informatics
Keywords Support Vector Machines; parameter setting; feature construction; Apriori; frequent patterns; object-oriented data
Description Support Vector Machines (SVM) are a machine learning algorithm that can be used for both classification and regression problems. In this paper, we focus on two problems with SVM. First, we concentrate on the parameter setting of SVM which has great influence on the performance. Then feature construction is discussed. Features can be used to improve results of SVM and to represent structured data in SVM. Mining frequent patterns from structured data is used to construct features.
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