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Efficient Classification for Additive Kernel SVMs

机译:附加内核SVM的有效分类

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We show that a class of nonlinear kernel SVMs admits approximate classifiers with runtime and memory complexity that is independent of the number of support vectors. This class of kernels, which we refer to as additive kernels, includes widely used kernels for histogram-based image comparison like intersection and chi-squared kernels. Additive kernel SVMs can offer significant improvements in accuracy over linear SVMs on a wide variety of tasks while having the same runtime, making them practical for large-scale recognition or real-time detection tasks. We present experiments on a variety of datasets, including the INRIA person, Daimler-Chrysler pedestrians, UIUC Cars, Caltech-101, MNIST, and USPS digits, to demonstrate the effectiveness of our method for efficient evaluation of SVMs with additive kernels. Since its introduction, our method has become integral to various state-of-the-art systems for PASCAL VOC object detection/image classification, ImageNet Challenge, TRECVID, etc. The techniques we propose can also be applied to settings where evaluation of weighted additive kernels is required, which include kernelized versions of PCA, LDA, regression, k-means, as well as speeding up the inner loop of SVM classifier training algorithms.
机译:我们表明,一类非线性内核SVM允许具有运行时间和内存复杂度的近似分类器,而这些分类器与支持向量的数量无关。这类内核,我们称为加性内核,包括广泛用于基于直方图的图像比较的内核,例如相交和卡方内核。附加内核SVM与线性SVM相比,在具有相同运行时间的同时,可以在多种任务上显着提高准确性,从而使其可用于大规模识别或实时检测任务。我们目前在各种数据集上进行实验,包括INRIA人,戴姆勒-克莱斯勒行人,UIUC汽车,Caltech-101,MNIST和USPS数字,以证明我们的方法有效评估具有附加核的SVM的有效性。自从引入以来,我们的方法已成为各种最新系统的组成部分,这些系统可用于PASCAL VOC对象检测/图像分类,ImageNet Challenge,TRECVID等。我们建议的技术也可以应用于评估加权添加剂的设置需要内核,包括PCA,LDA,回归,k-means的内核版本,以及加快SVM分类器训练算法的内部循环。

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