Practical large scale classification with additive kernels
Date of Issue2012
Asian Conference on Machine Learning, ACML (4th : 2012)
School of Computer Engineering
For classification problems with millions of training examples or dimensions, accuracy, training and testing speed and memory usage are the main concerns. Recent advances have allowed linear SVM to tackle problems with moderate time and space cost, but for many tasks in computer vision, additive kernels would have higher accuracies. In this paper, we propose the PmSVM-LUT algorithm that employs Look-Up Tables to boost the training and testing speed and save memory usage of additive kernel SVM classification, in order to meet the needs of large scale problems. The PmSVM-LUT algorithm is based on PmSVM (Wu, 2012), which employed polynomial approximation for the gradient function to speedup the dual coordinate descent method. We also analyze the polynomial approximation numerically to demonstrate its validity. Empirically, our algorithm is faster than PmSVM and feature mapping in many datasets with higher classification accuracies and can save up to 60% memory usage as well.
DRNTU::Engineering::Computer science and engineering
© 2012 The Authors(Journal of Machine Learning Research). This paper was published in Journal of Machine Learning Research and is made available as an electronic reprint (preprint) with permission of The Authors(Journal of Machine Learning Research). The paper can be found at the following official URL: [http://jmlr.org/proceedings/papers/v25/yang12/yang12.pdf]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.