کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407771 | 678168 | 2012 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Using Sequential Unconstrained Minimization Techniques to simplify SVM solvers
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
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چکیده انگلیسی
In this paper, we apply Sequential Unconstrained Minimization Techniques (SUMTs) to the classical formulations of both the classical L1 norm SVM and the least squares SVM. We show that each can be solved as a sequence of unconstrained optimization problems with only box constraints. We propose relaxed SVM and relaxed LSSVM formulations that correspond to a single problem in the corresponding SUMT sequence. We also propose a SMO like algorithm to solve the relaxed formulations that works by updating individual Lagrange multipliers. The methods yield comparable or better results on large benchmark datasets than classical SVM and LSSVM formulations, at substantially higher speeds.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 77, Issue 1, 1 February 2012, Pages 253–260
Journal: Neurocomputing - Volume 77, Issue 1, 1 February 2012, Pages 253–260
نویسندگان
Sachindra Joshi, Jayadeva, Ganesh Ramakrishnan, Suresh Chandra,