通过控制特定视图投票器的多样性和准确性的多视图提升
Multiview Boosting by Controlling the Diversity and the Accuracy of View-specific Voters
摘要：
在本文中，我们提出了一种基于提升的多视图学习算法，称为PB-MVBoost，它迭代地学习i）捕获特定于视图的信息的特定于视图的投票者的权重;和ii）通过优化PAC-Bayes多视图C-Bound来优化视图的权重，该C-Bound考虑了特定于视图的分类器的准确性和视图之间的多样性。我们推导出一个泛化界，这种策略的PAC贝叶斯理论，这是一个合适的工具来处理模型表示为一组选民的加权组合。在三个公开数据集上的不同实验表明，所提出的方法相对于最先进的模型的效率。
英文摘要：
In this paper we propose a boosting based multiview learning algorithm, referred to as PB-MVBoost, which iteratively learns i) weights over view-specific voters capturing view-specific information; and ii) weights over views by optimizing a PAC-Bayes multiview C-Bound that takes into account the accuracy of view-specific classifiers and the diversity between the views. We derive a generalization bound for this strategy following the PAC-Bayes theory which is a suitable tool to deal with models expressed as weighted combination over a set of voters. Different experiments on three publicly available datasets show the efficiency of the proposed approach with respect to state-of-art models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
