machine learningView dwtrueman's Other Codes
A new linear time solver for SVM which can be easily implemented with only several lines of MATLAB code, and can be easily parallelized.
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|Journal/Conference Name||The 31st International Conference on Machine Learning (ICML)|
|Paper Category||Computer Science|
|Paper 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 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-byvector 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 SVMper f, Pegasos and the LibLinear that integrates the TRON, PCD and DCD algorithms.|
|Date of publication||2014|
|Code Programming Language||C++|