Book ChapterDOI
A Criterion for Learning the Data-Dependent Kernel for Classification
Jun-Bao Li,Shu-Chuan Chu,Jeng-Shyang Pan +2 more
- pp 365-376
TLDR
A novel kernel optimization method based on maximum margin criterion is proposed, which can solve the problem of Xiong's work that the optimal solution can be solved by iteration update algorithm owing to the singular problem of matrix.Abstract:
A novel criterion, namely Maximum Margin Criterion (MMC), is proposed for learning the data-dependent kernel for classification. Different kernels create the different geometrical structures of the data in the feature space, and lead to different class discrimination. Selection of kernel influences greatly the performance of kernel learning. Optimizing kernel is an effective method to improve the classification performance. In this paper, we propose a novel kernel optimization method based on maximum margin criterion, which can solve the problem of Xiong's work [1] that the optimal solution can be solved by iteration update algorithm owing to the singular problem of matrix. Our method can obtain a unique optimal solution by solving an eigenvalue problem, and the performance is enhanced while time consuming is decreased. Experimental results show that the proposed algorithm gives a better performance and a lower time consuming compared with Xiong's work.read more
Citations
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Journal ArticleDOI
Learning SVM with weighted maximum margin criterion for classification of imbalanced data
TL;DR: A weighted maximum margin criterion is proposed to optimize the data-dependent kernel, which makes the minority class more clustered in the induced feature space and indicates the effectiveness of the proposed algorithm for imbalanced data classification problems.
References
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Proceedings ArticleDOI
Parameterisation of a stochastic model for human face identification
F.S. Samaria,Andy Harter +1 more
TL;DR: This paper presents a set of experimental results in which various HMM parameterisations are analysed and shows that stochastic modelling can be used successfully to encode feature information.