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Cited 46 time in webofscience Cited 55 time in scopus
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Equilibrium-based support vector machine for semisupervised classification SCIE SCOPUS

Title
Equilibrium-based support vector machine for semisupervised classification
Authors
Lee, DLee, J
Date Issued
2007-03
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGI
Abstract
A novel learning algorithm for semisupervised classification is proposed. The proposed method first constructs a support function that estimates a support of a data distribution using both labeled and unlabeled data. Then, it partitions a whole data space into a small number of disjoint regions with the aid of a dynamical system. Finally, it labels the decomposed regions utilizing the labeled data and the cluster structure described by the constructed support function. Simulation results show the effectiveness of the proposed method to label out-of-sample unlabeled test data as well as in-sample unlabeled data.
Keywords
dynamical systems; inductive learning; kernel methods; semisupervised learning; support vector machines (SVMs)
URI
https://oasis.postech.ac.kr/handle/2014.oak/23517
DOI
10.1109/TNN.2006.889
ISSN
1045-9227
Article Type
Article
Citation
IEEE TRANSACTIONS ON NEURAL NETWORKS, vol. 18, no. 2, page. 578 - 583, 2007-03
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이재욱LEE, JAEWOOK
Dept of Industrial & Management Enginrg
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