Automated parameter selection for support vector machine decision tree
- Authors
- Choi, Gyunghyun; Bae, Suk Joo
- Issue Date
- Oct-2006
- Publisher
- SPRINGER-VERLAG BERLIN
- Citation
- NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS, v.4233, pp.746 - 753
- Indexed
- SCIE
SCOPUS
- Journal Title
- NEURAL INFORMATION PROCESSING, PT 2, PROCEEDINGS
- Volume
- 4233
- Start Page
- 746
- End Page
- 753
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/180923
- DOI
- 10.1007/11893257_83
- ISSN
- 0302-9743
- Abstract
- A support vector machine (SVM) provides an optimal separating hyperplane between two classes to be separated. However, the SVM gives only recognition results such as a neural network in a black-box structure. As an alternative, support vector machine decision tree (SVDT) provides useful information on key attributes while taking a number of advantages of the SVM. we propose an automated parameter selection scheme in SVDT to improve efficiency and accuracy in classification problems. Two practical applications confirm that the proposed methods has a potential in improving generalization and classification error in SVDT.
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Collections - 서울 공과대학 > 서울 산업공학과 > 1. Journal Articles
- 서울 기술경영전문대학원 > 서울 기술경영학과 > 1. Journal Articles
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