.png){kind=icon}
返回
A Boosting Approach to Exploit Instance Correlations for Multi-Instance Classification
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2015.2497318.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We propose a Boosting approach for multi-instance (MI) classification. L-p-norm is integrated to localize the witness instances and formulate the bag scores from classifier outputs. The contributions are twofold. First, a flexible and concise model for Boosting is proposed by the L-p-norm localization and exponential loss optimization. The scores for bag-level classification are directly fused from the instance feature space without probabilistic assumptions. Second, gradient and Newton descent optimizations are applied to derive the weak learners for Boosting. In particular, the instance correlations are exploited by fitting the weights and Newton updates for the weak learner construction. The final Boosted classifiers are the sums of iteratively chosen weak learners. Experiments demonstrate that the proposed L-p-norm-localized Boosting approach significantly improves the MI classification performance. Compared with the state of the art, the approach achieves the highest MI classification accuracy on 7/10 benchmark data sets.
Keyword:
Boosting
instance correlations
L-p-norm-based localization
multi-instance (MI) classification
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:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
.png){kind=icon}
Image Annotation By Multiple-Instance Learning With Discriminative Feature Mapping and Selection基于判别特征映射和选择的多示例学习图像标注
PREAI
分享收藏
查看更多内容