Universum-support vector machine (U-SVM) is an elegant method for 2-class classification problem. It is systematically studied in this paper, including the existence and uniqueness of the primal problem as well as the relation between the solutions of primal problem and dual problem. We find that U-SVM uses 3-class classification approach to solve the 2-class classification problem. So we have ...

The reduced support vector machine (RSVM) has been proposed to avoid the computational difficulties in generating a nonlinear support vector machine classifier for a massive dataset. RSVM selects a small random subset from the entire dataset with a user pre-specified size m̄ to generate a reduced kernel (rectangular) matrix. This reduced kernel will replace the fully dense square kernel matrix u...

We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with...

Support vector machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed support vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularizat...

Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin. Therefore, C-SVM loses robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according t...

Acknowledgements This work was written as my diploma thesis in mathematics at the University of Hei-delberg under the supervision of Prof. Dr. Enno Mammen. I wish to thank him for his help and advice. In Tobias Müller I found a very competent advisor. I owe him much for his assistance in all aspects of my work. Thank you very much! Dr. Lutz Edler, head of the Biostatistics Unit at the DKFZ, kin...

In classification problems classes usually have different geometrical structure and therefore it seems natural for each class to have its own margin type. Existing methods using this principle lead to the construction of the different (from SVM) optimization problems. Although they outperform the standard model, they also prevent the utilization of existing SVM libraries. We propose an approach...

This paper describes our system for participating in the system validation subtask of NTCIR-11 RITE-VAL. We trained a SVM model with LibSVM using features extracted from labeled sentence pairs. Besides features based on lexical, syntactic and semantic analysis, we introduce a novel approach of extracting “concepts” from a sentence and generating features based on it. Unlabeled testing sentence ...