.png){kind=icon}
返回
Discriminatively regularized least-squares classification
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2008.07.010.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Over the past decades, regularization theory is widely applied in various areas of machine learning to derive a large family of novel algorithms. Traditionally, regularization focuses Oil smoothing only, and does not fully Utilize the Underlying discriminative knowledge which is vital for classification. fit this paper, we propose a novel regularization algorithm in the least-squares sense, called discriminatively regularized least-squares classification (DRLSC) method, which is specifically designed for classification. Inspired by several new geometrically Motivated methods, DRLSC directly embeds the discriminative information as well as the local geometry of the samples into the regularization term so that it can explore as much underlying knowledge inside the samples as possible and aim to maximize the margins between the samples of different classes in each local area. Furthermore, by embedding equality type constraints in the formulation, the solutions of DRLSC can follow from solving a set of linear equations and the framework naturally contains multi-class Problems. Experiments on both toy and real world problems demonstrate that DRLSC is often superior in classification performance to the classical regularization algorithms, including regularization networks, support vector machines and some of the recent studied manifold regularization techniques. (C) 2008 Elsevier Ltd. All rights reserved.
Keyword:
Classifier design
Discriminative information
Manifold learning
Pattern recognition
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:
7.6
论文数:
1.3W
被引数:
4.5W
机构
暂无机构信息
引用论文
.png){kind=icon}
Salt-and-pepper noise removal by median-type noise detectors and detail-preserving regularization通过中值型噪声检测器和细节保持正则化去除椒盐噪声
PREAI
分享收藏
查看更多内容