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Multiple Kernel Learning by Gap Maximization between Classes in RKHS : 커널 공간에서 클래스 샘플들간의 상대적 거리 최대화로 구현한 커널 학습 방법론
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- Authors
- Advisor
- 곽노준
- Major
- 융합과학기술대학원 융합과학부
- Issue Date
- 2016-02
- Publisher
- 서울대학교 융합과학기술대학원
- Keywords
- Multiple Kernel Learning ; Reproducing Kernel Hilbert Space ; Kernel Method ; Kernel Alignment ; Kernel Trick
- Description
- 학위논문 (석사)-- 서울대학교 융합과학기술대학원 : 융합과학기술대학원 융합과학부 지능형융합시스템학 전공, 2016. 2. 곽노준.
- Abstract
- By implicitly maximizing the gap between classes in the reproducing kernel Hilbert
space (RKHS), a multiple kernel learning (MKL) is formulated as a linear programming
in this paper.
For each sample, my method tries to enforce the distance between intra-class and
inter-class samples in RKHS to be as distant as possible. n my method, each training sample imposes at most r constraints for the linear programming where r is the number of different kernel types. Unlike previous methods of multiple kernel learning, the proposed method does not need a large amount of computations. My method is compared with various methods of MKL to prove its efficiency of finding a good kernel mixture parameter.
- Language
- English
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