Generalized SMO Algorithm for SVM-Based Multitask Learning

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 gener...

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Veröffentlicht in:	IEEE transaction on neural networks and learning systems Jg. 23; H. 6; S. 997 - 1003
Hauptverfasser:	Feng Cai, Cherkassky, V.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York, NY IEEE 01.06.2012 Institute of Electrical and Electronics Engineers The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Algorithms Applied sciences Artificial intelligence Classification Computational efficiency Computer science; control theory; systems Data processing. List processing. Character string processing Exact sciences and technology Formulations Learning Learning and adaptive systems learning with structured data Machine learning Memory organisation. Data processing multitask learning Optimization quadratic optimization sequential minimal optimization Software Studies support vector machine (SVM) Support vector machines Training Training data Vectors Zirconium {\rm SVM}{+} Sample size support vector machine (SVM) sequential minimal optimization learning with structured data SVM Quadratic programming multitask learning quadratic optimization Inductive learning Optimization Active system Supervised learning Sequential method Internal structure Concurrent task Classification Vector support machine Probability learning Data structure Inductive logic programming Learning algorithm Artificial intelligence Mathematical programming
ISSN:	2162-237X, 2162-2388
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Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	2162-237X 2162-2388
DOI:	10.1109/TNNLS.2012.2187307