DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Sung, Youngchul | - |
dc.contributor.advisor | 성영철 | - |
dc.contributor.author | Jung, Whiyoung | - |
dc.date.accessioned | 2018-06-20T06:22:27Z | - |
dc.date.available | 2018-06-20T06:22:27Z | - |
dc.date.issued | 2017 | - |
dc.identifier.uri | http://library.kaist.ac.kr/search/detail/view.do?bibCtrlNo=675434&flag=dissertation | en_US |
dc.identifier.uri | http://hdl.handle.net/10203/243326 | - |
dc.description | 학위논문(석사) - 한국과학기술원 : 전기및전자공학부, 2017.2,[iii, 24 p. :] | - |
dc.description.abstract | Support Vector Machine(SVM) have been developed in many ways after the soft-margin SVM had been suggested in 1995. Bayesian SVM and Universum SVM are two methods for developing SVM. Bayesian SVM interpreting in probabilistic frameworks, samples classifiers from the particular posterior distribution. Bayesian SVM classifies new data by averaging the results of the sampled classifiers, and it have better performance than the soft-margin SVM for some data. Universum SVM which is trained by not only training data but also additory data called universum data, has better performance than soft-margin SVM. In this paper, we suggest a probabilistic interpretation of Universum SVM, and we use bayesian method to Universum SVM. In addition, we compare the performance of bayesian Universum SVM and Universum SVM. As it is denoted that the importance of selecting the universum data in the paper which suggested Universum SVM, solving the problem selecting the universum data under the probabilistic model will be done in our future work. | - |
dc.language | eng | - |
dc.publisher | 한국과학기술원 | - |
dc.subject | Universum SVM | - |
dc.subject | Support Vector Machine | - |
dc.subject | Bayesian SVM | - |
dc.subject | Universum Learning | - |
dc.subject | Bayesian Universum SVM | - |
dc.subject | 유니벌썸 서포트 벡터 머신 | - |
dc.subject | 서포트 벡터 머신 | - |
dc.subject | 베이지안 서포트 벡터 머신 | - |
dc.subject | 유니벌썸 학습 | - |
dc.subject | 베이지안 유니벌썸 서포트 벡터 머신 | - |
dc.title | (A) bayesian approach to universum support vector machine | - |
dc.title.alternative | 유니벌썸 서포트 벡터 머신의 베이지안 접근 방법 | - |
dc.type | Thesis(Master) | - |
dc.identifier.CNRN | 325007 | - |
dc.description.department | 한국과학기술원 :전기및전자공학부, | - |
dc.contributor.alternativeauthor | 정휘영 | - |
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