Indexed Optimization: Learning Ramp-Loss SVM in Sublinear Time

碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === Multidimensional indexing has been frequently used for sublinear-time nearest neighbor search in various applications. In this paper, we demonstrate how this technique can be integrated into learning problem with sublinear sparsity like ramp-loss SVM. We propos...

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Bibliographic Details
Main Authors: EN-HSU YEN, 嚴恩勗
Other Authors: 林守德
Format: Others
Language:en_US
Published: 2012
Online Access:http://ndltd.ncl.edu.tw/handle/73543025733430311023
Description
Summary:碩士 === 國立臺灣大學 === 資訊工程學研究所 === 100 === Multidimensional indexing has been frequently used for sublinear-time nearest neighbor search in various applications. In this paper, we demonstrate how this technique can be integrated into learning problem with sublinear sparsity like ramp-loss SVM. We propose an outlier-free convex-relaxation for ramp-loss SVM and an indexed optimization algorithm which solves large-scale problem in sublinear-time even when data cannot fit into memory. We compare our algorithm with state-of-the-art linear hinge-loss solver and ramp-loss solver in both sufficient and limited memory conditions, where our algorithm not only learns several times faster but achieves more accurate result on noisy and large-scale datasets.