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建筑的稀疏支持向量机的多实例的分类
Building Sparse Support Vector Machines for Multi-Instance Classification |
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| 课程网址: |
http://videolectures.net/ecmlpkdd2011_fu_building/
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| 主讲教师: |
Zhouyu Fu
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| 开课单位: |
莫纳什大学
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| 开课时间: |
2011-10-03
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| 课程语种: |
英语
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| 中文简介: |
我们提出了一种直接方法来学习用于多实例（MI）分类的稀疏支持向量机（SVM）预测模型。所提出的稀疏SVM基于“标签均值”。 MI分类的制定，其取出用于袋级预测的个体实例的预测的平均值。这导致了凸优化问题，这对于我们随后导出的稀疏SVM公式所产生的优化问题的易处理性以及我们用于解决它的优化策略的有效性是必不可少的。基于“标签均值”和“标签均值”。在制定中，我们可以构建用于MI分类的稀疏SVM模型，并通过强制执行预测函数中允许的最大扩展数来明确控制它们的稀疏性。采用有效的优化策略来解决制定的稀疏学习问题，该问题涉及分类器和扩展矢量的学习。基准数据集的实验结果表明，所提出的方法在构建非常稀疏的SVM模型时是有效的，同时实现了与最先进的MI分类器相当的性能。
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| 课程简介: |
We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for Multi-Instance (MI) classification. The proposed sparse SVM is based on a "label-mean" formulation of MI classification which takes the average of predictions of individual instances for bag-level prediction. This leads to a convex optimization problem, which is essential for the tractability of the optimization problem arising from the sparse SVM formulation we derived subsequently, as well as the validity of the optimization strategy we employed to solve it. Based on the "label-mean" formulation, we can build sparse SVM models for MI classification and explicitly control their sparsities by enforcing the maximum number of expansions allowed in the prediction function. An effective optimization strategy is adopted to solve the formulated sparse learning problem which involves the learning of both the classifier and the expansion vectors. Experimental results on benchmark data sets have demonstrated that the proposed approach is effective in building very sparse SVM models while achieving comparable performance to the state-of-the-art MI classifiers.
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| 关 键 词: |
支持向量机; 稀疏的SVM模型和; 扩展向量学习
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-06-24：yumf
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| 阅读次数: |
65
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