کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
408610 679036 2007 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Analysis of SVM regression bounds for variable ranking
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Analysis of SVM regression bounds for variable ranking
چکیده انگلیسی

This paper addresses the problem of variable ranking for support vector regression. The ranking criteria that we proposed are based on leave-one-out bounds and some variants and for these criteria we have compared different search-space algorithms: recursive feature elimination and scaling factor optimization based on gradient-descent. All these algorithms have been compared on toy problems and real-world QSAR data sets. Results show that the radius-margin criterion is the most efficient criterion for ranking variables. Using this criterion can then lead to support vector regressor with improved error rate while using fewer variables. Our results also support the evidence that gradient-descent algorithm achieves a better variable ranking compared to backward algorithm.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 7–9, March 2007, Pages 1489–1501
نویسندگان
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