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正交多实例内核
Conformal Multi-Instance Kernels |
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| 课程网址: |
http://videolectures.net/lce06_blaschko_cmik/
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| 主讲教师: |
Matthew B. Blaschko
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| 开课单位: |
哥伦比亚大学
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| 开课时间: |
2007-04-16
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| 课程语种: |
英语
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| 中文简介: |
在多实例学习设置中，每个观察是一包特征向量，其中一个或多个向量指示类中的成员资格。主要任务是确定包中的任何向量是否指示类成员资格，而忽略不包含的向量。我们在这里描述了一种基于内核的技术，该技术通过共形变换定义了一个参数系列的内核，并联合学习了包上的判别函数以及内核的最佳参数设置。学习共形变换有效地相当于在特征空间中根据它们对分类准确性的贡献来加权区域;具有歧视性的区域的权重高于非区域的区域。这允许分类器关注有助于分类准确性的区域，同时忽略对应于在正袋和负袋中找到的载体的区域。我们展示了如何通过将问题作为多核学习问题来学习支持向量机的这种转换的参数。生成的多实例分类器为来自不同域的多个多实例基准数据集提供了竞争准确性。
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| 课程简介: |
In the multiple instance learning setting, each observation is a bag of feature vectors of which one or more vectors indicates membership in a class. The primary task is to identify if any vectors in the bag indicate class membership while ignoring vectors that do not. We describe here a kernel-based technique that defines a parametric family of kernels via conformal transformations and jointly learns a discriminant function over bags together with the optimal parameter settings of the kernel. Learning a conformal transformation effectively amounts to weighting regions in the feature space according to their contribution to classification accuracy; regions that are discriminative will be weighted higher than regions that are not. This allows the classifier to focus on regions contributing to classification accuracy while ignoring regions that correspond to vectors found both in positive and in negative bags. We show how parameters of this transformation can be learned for support vector machines by posing the problem as a multiple kernel learning problem. The resulting multiple instance classifier gives competitive accuracy for several multi-instance benchmark datasets from different domains.
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| 关 键 词: |
内核; 判别函数; 分类器
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-05-12：lxf
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| 阅读次数: |
66
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