Multi-category laplacian least squares twin support vector machine

被引：0
作者：
Reshma Khemchandani
Aman Pal
机构：
[1] South Asian University,
来源：
Applied Intelligence | 2016年 / 45卷
关键词：
Laplacian support vector machine; Least squares; Twin support vector machine; Multi-class classification;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, we have formulated a Laplacian Least Squares Twin Support Vector Machine called Lap-LST-KSVC for semi-supervised multi-category k-class classification problem. Similar to Least Squares Twin Support Vector Machine for multi-classification(LST-KSVC), Lap-LST-KSVC, evaluates all the training samples into “1-versus-1-versus-rest” classification paradigm, so as to generate ternary output {−1, 0, +1}. Experimental results prove the efficacy of the proposed method over other inline Laplacian Twin Support Vector Machine(Lap-TWSVM) in terms of classification accuracy and computational time.
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页码：458 / 474
页数：16
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