کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
393473 665653 2014 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Alternative second-order cone programming formulations for support vector classification
ترجمه فارسی عنوان
فرمول های برنامه نویسی مخروطی جایگزین دوم برای طبقه بندی بردار پشتیبانی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی
This paper presents two novel second-order cone programming (SOCP) formulations that determine a linear predictor using Support Vector Machines (SVMs). Inspired by the soft-margin SVM formulation, our first approach (ξ-SOCP-SVM) proposes a relaxation of the conic constraints via a slack variable, penalizing it in the objective function. The second formulation (r-SOCP-SVM) is based on the LP-SVM formulation principle: the bound of the VC dimension is loosened properly using the l∞-norm, and the margin is directly maximized. The proposed methods have several advantages: The first approach constructs a flexible classifier, extending the benefits of the soft-margin SVM formulation to second-order cones. The second method obtains comparable results to the SOCP-SVM formulation with less computational effort, since one conic restriction is eliminated. Experiments on well-known benchmark datasets from the UCI Repository demonstrate that our approach accomplishes the best classification performance compared to the traditional SOCP-SVM formulation, LP-SVM, and to standard linear SVM.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 268, 1 June 2014, Pages 328-341
نویسندگان
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