.png){kind=icon}
返回
A study on regularized Weighted Least Square Support Vector Classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2018.03.002.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Least Square Support Vector Machine(LSSVM) has been widely used for solving regression and classification problems due to its simple solution. However, LSSVM has some drawbacks in practice, such as lack of robustness, loss of sparseness of its solution and inapplicability of solving classification problem in some cases. In order to find robust and sparse solution for classification problem by using Least Square methods, we propose a novel regularized Weighted Least Square Support Vector Classifier in this paper. First, we give a basic optimized Weighted Least Square Support Vector Classifier model, which can be used to obtain the best weights according to the distance between samples and classification boundary and obtain an extremely sparse solution. In order to control the sparsity of solution, we further propose a regularized Weighted Least Square Support Vector Classifier model. After theoretical analysis of regularization function, we construct 2 lands of regularization functions which meet our requirements and then design relative algorithms for optimizing weights and finding the best solution. The proposed method is evaluated on artificial datasets and several important benchmark databases in Machine Learning, and achieves the more encouraging results compared with some state-of-the-art approaches. (C) 2018 Elsevier B.V. All rights reserved.
Keyword:
Weighted Least Square Support Vector
Classifier
Regularization technique
Robust estimation
Sparse classifier
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.3
论文数:
8.0K
被引数:
1.6W
机构
引用论文
Robust regularized extreme learning machine for regression using iteratively reweighted least squares使用迭代加权最小二乘的鲁棒正则化极限学习机回归.png)
NEUROCOMPUTING
NEUROCOMPUTINGIF6.5
PREAI
分享收藏分享收藏
查看更多内容