서지주요정보
계층적 SVM을 이용한 다중 클래스 분류 = Hierarchical binary decision tree with SVM for multi-class classification
서명 / 저자 계층적 SVM을 이용한 다중 클래스 분류 = Hierarchical binary decision tree with SVM for multi-class classification / 정성문.
발행사항 [대전 : 한국과학기술원, 2004].
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소장정보

등록번호

8015230

소장위치/청구기호

학술문화관(문화관) 보존서고

MEE 04078

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리뷰정보

초록정보

In this paper, I proposed a method to classify multi-class problem. To achieve that goal I proposed a modified SOM(Self-Organizing Map), so called K-SOM(Kernel Self-Organizing Map), and how to use the result of SOM to combine it with Support Vector Machine. SOM is an approach to extract a topological relationship among data. And SVM is the newest method for classification. However, SVM deals data not on the original data space but on the transformed kernel space. In this case, transformed kernel space has much higher or infinite dimension than original data space. If we select proper kernel function and then on kernel space transformed data distribution is well formed and linearly separable-like, SVM can provide much better classification accuracy than on original data space. To combine SOM with SVM, space matching is necessary. That is, SOM is also analyzed on the kernel space. It is more reasonable and guarantee much better clustering performance than conventional Kohonen SOM. From the SOM result, I constructed a hierarchical decision tree by using scattering measure. SVM can solve only binary classification problem. Therefore we need to original problem to the combination of binary sub-problems. In this process, allowing overlapping between groups can provide a chance to correct the misclassified data again. Because SOM result represent the relationship among class, applying a proper measure on this result can give hierarchical decision tree easily. Most of errors on classification problem are occurred on the confusing area. By allowing overlapping same class between two groups, hierarchical SVM scheme provide a better performance and from the structural properties of tree H-SVM give a better speed to classify data.

서지기타정보

서지기타정보
청구기호 {MEE 04078
형태사항 vi, 56 p. : 삽도 ; 26 cm
언어 한국어
일반주기 저자명의 영문표기 : Sung-Moon Cheong
지도교수의 한글표기 : 이수영
지도교수의 영문표기 : Soo-Young Lee
학위논문 학위논문(석사) - 한국과학기술원 : 전기및전자공학전공,
서지주기 참고문헌 : p. 55-56
주제 분류
다중클래스
기계학습
자가조직화
커널
CLASSIFICATION
MULTI-CLASS
SVM
SOM
KERNEL
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