Feature construction and parameter setting for Support Vector Machines
| Authors | |
|---|---|
| Year of publication | 2003 |
| Type | Article in Proceedings |
| Conference | Proceedings of the 2nd Conference Znalosti 2003 |
| MU Faculty or unit | |
| 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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