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By the use of the<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mrow><mml:msub><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn>1<\/mml:mn><mml:mo>\/<\/mml:mo><mml:mn>2<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>regularization technique, we propose a new model<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:msub><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn>1<\/mml:mn><mml:mo>\/<\/mml:mo><mml:mn>2<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>-SVM. To solve this nonconvex and non-Lipschitz optimization problem, we first transform it into an equivalent quadratic constrained optimization model with linear objective function and then develop an interior point algorithm. We establish the convergence of the proposed algorithm. Our experiments with artificial data and real data demonstrate that the<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:msub><mml:mrow><mml:mi>L<\/mml:mi><\/mml:mrow><mml:mrow><mml:mn>1<\/mml:mn><mml:mo>\/<\/mml:mo><mml:mn>2<\/mml:mn><\/mml:mrow><\/mml:msub><\/mml:mrow><\/mml:math>-SVM model works well and the proposed algorithm is more effective than some popular methods in selecting relevant features and improving classification performance.<\/jats:p>","DOI":"10.1155\/2014\/942520","type":"journal-article","created":{"date-parts":[[2014,4,10]],"date-time":"2014-04-10T21:05:37Z","timestamp":1397163937000},"page":"1-16","source":"Crossref","is-referenced-by-count":4,"title":["An Interior Point Method for<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" 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