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Generalized SMO Algorithm for SVM-Based Multitask Learning
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DOI：10.1109/TNNLS.2012.2187307.png)
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Abstract
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Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed a general approach to formalizing such problems, known as learning with structured data and its support vector machine (SVM) based optimization formulation called SVM+. Liang and Cherkassky showed the connection between SVM+ and multitask learning (MTL) approaches in machine learning, and proposed an SVM-based formulation for MTL called SVM+ MTL for classification. Training the SVM+ MTL classifier requires the solution of a large quadratic programming optimization problem which scales as O(n(3)) with sample size n. So there is a need to develop computationally efficient algorithms for implementing SVM+ MTL. This brief generalizes Platt's sequential minimal optimization (SMO) algorithm to the SVM+ MTL setting. Empirical results show that, for typical SVM+ MTL problems, the proposed generalized SMO achieves over 100 times speed-up, in comparison with general-purpose optimization routines.
Keywords:
Classification
learning with structured data
multitask learning
quadratic optimization
sequential minimal optimization
support vector machine (SVM)
SVM
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