图 书 馆
一种基于直方图交集核的高效支持向量机分类方法A Novel Approach for Efficient SVM Classification with Histogram Intersection Kernel |
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| 课程网址: | http://videolectures.net/bmvc2013_sharma_novel_approach/ |
| 主讲教师: | Gaurav Sharma |
| 开课单位: | 特艺集团 |
| 开课时间: | 2014-04-03 |
| 课程语种: | 英语 |
| 中文简介: |  内核技巧-通常用于机器学习和计算机视觉中-使得能够学习非线性决策函数，而不必将原始数据显式映射到高维空间。但是，在测试时，它需要使用每个支持向量来评估内核，这非常耗时。在本文中，我们提出了一种无需使用核技巧就可以学习与直方图相交核相对应的非线性SVM的新方法。我们在原始空间中公式化精确的非线性问题，并展示如何在该空间中直接执行分类。与使用内核技巧时的O（d Nsv）相比，学习型分类器在保持O（d）测试复杂度（针对d维输入空间）的同时纳入了非线性。我们证明了带有直方图相交核的SVM问题在输入空间中是准凸的，并概述了一个迭代算法来解决。与其他基于线性SVM的方法进行了比较，该方法已在实验中得到了验证，表明该方法在较低的计算和内存成本下可以达到相似或更好的性能。 |
| 课程简介: | The kernel trick – commonly used in machine learning and computer vision – enables learning of non-linear decision functions without having to explicitly map the original data to a high dimensional space. However, at test time, it requires evaluating the kernel with each one of the support vectors, which is time consuming. In this paper, we propose a novel approach for learning non-linear SVM corresponding to the histogram intersection kernel without using the kernel trick. We formulate the exact non-linear problem in the original space and show how to perform classification directly in this space. The learnt classifier incorporates non-linearity while maintaining O(d) testing complexity (for d-dimensional input space), compared to O(d Nsv) when using the kernel trick. We show that the SVM problem with histogram intersection kernel is quasi-convex in input space and outline an iterative algorithm to solve it. The proposed approach has been validated in experiments where it is compared with other linear SVM-based methods, showing that the proposed method achieves similar or better performance at lower computational and memory costs. |
| 关 键 词: | 非线性问题; 机器学习 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2020-11-26：zyk |
| 最后编审: | 2020-11-26：zyk |
| 阅读次数: | 29 |