.png){kind=icon}
返回
An active learning-based SVM multi-class classification model
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patcog.2014.12.009.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Traditional multi-class classification models are based on labeled data and are not applicable to unlabeled data. To overcome this limitation, this paper presents a multi-class classification model that is based on active learning and support vector machines (MC_SVMA), which can be used to address unlabeled data. Firstly, a number of unlabeled samples are selected as the most valuable samples using the active learning technique. And then, the model quickly mines the pattern classes for unlabeled samples by computing the differences between the unlabeled and labeled samples. Moreover, to label the unlabeled samples accurately and acquire more class information, the active learning strategy is also used to select compatible, rejected and uncertain samples, which are labeled by experts. Thus, the proposed model can determine as many classes as possible while requiring fewer samples to be manually labeled. This approach permits an unlabeled multi-classification problem to be translated into a classical supervised multi-classification problem. The experimental results demonstrate that the MC_SVMA model is efficient and exhibits good generalization performance. (C) 2014 Elsevier Ltd. All rights reserved.
Keyword:
Multi-class classification with unknown categories
Active learning
Support vector machine
MC_SVMA model
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
引用论文
.png){kind=icon}
查看更多内容