Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
402263 | Knowledge-Based Systems | 2015 | 10 Pages |
Abstract
In this paper, we propose a novel sparse and robust nonparallel hyperplane classifier, named Ramp loss Nonparallel Support Vector Machine (RNPSVM), for binary classification. By introducing the Ramp loss function and also proposing a new non-convex and non-differentiable loss function based on the ε-insensitive loss function, RNPSVM can explicitly incorporate noise and outlier suppression in the training process, has less support vectors and the increased sparsity leads to its better scaling properties. The non-convexity of RNPSVM can be efficiently solved by the Concave–Convex Procedure and experimental results on benchmark datasets confirm the effectiveness of the proposed algorithm.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Dalian Liu, Yong Shi, Yingjie Tian,