کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406655 678103 2012 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Accurate on-line νν-support vector learning
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Accurate on-line νν-support vector learning
چکیده انگلیسی

The νν-Support Vector Machine (νν-SVM) for classification proposed by Schölkopf et al. has the advantage of using a parameter νν on controlling the number of support vectors and margin errors. However, comparing to standard CC-Support Vector Machine (CC-SVM), its formulation is more complicated, up until now there are no effective methods on solving accurate on-line learning for it. In this paper, we propose a new effective accurate on-line algorithm which is designed based on a modified formulation of the original νν-SVM. The accurate on-line algorithm includes two special steps: the first one is relaxed adiabatic incremental adjustments; the second one is strict restoration adjustments. The experiments on several benchmark datasets demonstrate that using these two steps the accurate on-line algorithm can avoid the infeasible updating path as far as possible, and successfully converge to the optimal solution. It achieves the fast convergence especially on the Gaussian kernel and is faster than the batch algorithm.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 27, March 2012, Pages 51–59
نویسندگان
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