.png){kind=icon}
返回
An MIMLSVM algorithm based on ECC
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-014-0608-z.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In the multi-instance multi-label learning framework, an example is described by multiple instances and associated with multiple class labels at the same time. An idea of tackling with multi-instance multi-label problems is to identify its equivalence in the traditional supervised learning framework. However, some useful information such as the correlation between labels may be lost in the process of degeneration, which will influence the classification performance. In E-MIMLSVM+ algorithm, multi-task learning techniques are utilized to incorporate label correlations, while it is time consuming as well as memory consuming. Therefore, we propose an improved algorithm. In our algorithm, the classifier chains method is applied in E-MIMLSVM+ to incorporate label correlations instead of multi-task learning techniques. The experimental results show that the proposed algorithm can reduce time complexity and improve the predictive performance.
Keyword:
Support vector machine
Multi-instance multi-label
Classifier chains
Label correlations
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:
3.5
论文数:
7.7K
被引数:
1.9W
机构
引用论文
Manifold proximal support vector machine for semi-supervised classification.png)
APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏The incremental learning algorithm with support vector machine based on hyperplane-distance基于超平面距离的支持向量机增量学习算法APPLIED INTELLIGENCE
APPLIED INTELLIGENCEIF3.5
PREAI
分享收藏Clinical significance of PD-L1 and PD-L2 copy number gains in non-small-cell lung cancerOncotarget
OAAI
OncotargetIF0
OAAI分享收藏
查看更多内容