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Linear Time Solver for Primal SVM
Authors: Feiping Nie, Yizhen Huang and Heng Huang
Conference: Proceedings of the 31st International Conference on Machine Learning (ICML-14)
Abstract: Support Vector Machines (SVM) is among the most popular classification techniques in machine learning, hence designing fast primal SVM algorithms for large-scale datasets is a hot topic in recent years. This paper presents a new L2-norm regularized primal SVM solver using Augmented Lagrange Multipliers, with linear-time computational cost for Lp-norm loss functions. The most computationally intensive steps (that determine the algorithmic complexity) of the proposed algorithm is purely and simply matrix-by-vector multiplication, which can be easily parallelized on a multi-core server for parallel computing. We implement and integrate our algorithm into the interfaces and framework of the well-known LibLinear software toolbox. Experiments show that our algorithm is with stable performance and on average faster than the state-of-the-art solvers such as SVMperf , Pegasos and the LibLinear that integrates the TRON, PCD and DCD algorithms.
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