• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.159-170
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• 2005

OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

• Journal of Internet Computing and Services
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• v.12 no.5
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• pp.87-100
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• 2011

In this paper, we address a video summarization task as generating both visually salient and semantically important video segments. In order to find salient data points, one can use the OC-SVM (One-class Support Vector Machine), which is well known for novelty detection problems. It is, however, hard to incorporate into the OC-SVM process the importance measure of data points, which is crucial for video summarization. In order to integrate the importance of each point in the OC-SVM process, we propose a fuzzy version of OC-SVM. The Importance-based Fuzzy OC-SVM weights data points according to the importance measure of the video segments and then estimates the support of a distribution of the weighted feature vectors. The estimated support vectors form the descriptive segments that best delineate the underlying video content in terms of the importance and salience of video segments. We demonstrate the performance of our algorithm on several synthesized data sets and different types of videos in order to show the efficacy of the proposed algorithm. Experimental results showed that our approach outperformed the well known traditional method.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.123-123
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• 2003

More elaborated methods allowing the usage of binary classifiers for the resolution of multi-class classification problems are briefly presented. This way of using FSVC to learn a K-class classification problem consists in choosing the maximum applied to the outputs of K FSVC solving a one-per-class decomposition of the general problem.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.5
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• pp.716-721
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• 2010

In this work we address the use of support vector machine (SVM) in the multi-class gas classification system. The objective is to classify single gases and their mixture with a semiconductor-type electronic nose. The SVM has some typical multi-class classification models; One vs. One (OVO) and One vs. All (OVA). However, studies on those models show weaknesses on calculation time, decision time and the reject region. We propose a hierarchical clustering method (HCM) based on the SVM for real-time gas mixture classification. Experimental results show that the proposed method has better performance than the typical multi-class systems based on the SVM, and that the proposed method can classify single gases and their mixture easily and fast in the embedded system compared with BP-MLP and Fuzzy ARTMAP.