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用于分类的度量空间的最佳维数
Optimal Dimensionality of Metric Space for Classification |
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| 课程网址: |
http://videolectures.net/icml07_xiangyang_odm/
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| 主讲教师: |
Xiangyang Xue
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| 开课单位: |
复旦大学
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| 开课时间: |
2007-07-27
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| 课程语种: |
英语
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| 中文简介: |
对于大规模的分类问题, 训练样本可以事先聚集为下采样预处理, 然后只使用得到的聚类进行训练。在这种假设的推动下, 我们在 vapnik 引入的学习框架内提出了一种分类算法--支持集群机 (scm)。对于 scm, 采用了兼容的内核, 这样不仅可以在训练阶段的集群之间处理相似度度量, 还可以在测试阶段的集群和向量之间进行处理。我们还证明了 scm 是支持向量机的一个通用扩展与 rbf 内核。实验结果证实, 由于大大降低了训练和测试的计算成本以及可比的分类精度, scm 对大规模分类问题非常有效。作为一个副产品, 它为处理隐私保护数据挖掘问题提供了一种很有前途的方法。
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| 课程简介: |
For large-scale classification problems, the training samples can be clustered beforehand as a downsampling pre-process, and then only the obtained clusters are used for training. Motivated by such assumption, we proposed a classification algorithm, Support Cluster Machine (SCM), within the learning framework introduced by Vapnik. For the SCM, a compatible kernel is adopted such that a similarity measure can be handled not only between clusters in the training phase but also between a cluster and a vector in the testing phase. We also proved that the SCM is a general extension of the SVM with the RBF kernel. The experimental results confirm that the SCM is very effective for largescale classification problems due to significantly reduced computational costs for both training and testing and comparable classification accuracies. As a by-product, it provides a promising approach to dealing with privacy-preserving data mining problems.
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| 关 键 词: |
供应链管理; 核支持向量机; 聚类; UCI声纳数据集
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| 课程来源: |
视频讲座网
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| 最后编审: |
2020-07-06：heyf
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| 阅读次数: |
42
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