کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10362220 | 870657 | 2005 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Efficient performance estimate for one-class support vector machine
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
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چکیده انگلیسی
This letter proposes and analyzes a method (ξαÏ-estimate) to estimate the generalization performance of one-class support vector machine (SVM) for novelty detection. The method is an extended version of the ξα-estimate method, which is used to estimate the generalization performance of standard SVM for classification. Our method is derived from analyzing the connection between one-class SVM and standard SVM. Without any computation intensive re-sampling, the method is computationally much more efficient than leave-one-out method, since it can be computed immediately from the decision function of one-class SVM. Using our method to estimate the error rate is more precise than using the fraction of support vectors and a parameter ν of one-class SVM. We also propose that the fraction of support vectors characterizes the precision of one-class SVM. A theoretical analysis and experiments on an artificial data and a widely known handwritten digit recognition set (MNIST) show that our method can effectively estimate the generalization performance of one-class SVM for novelty detection.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 26, Issue 8, June 2005, Pages 1174-1182
Journal: Pattern Recognition Letters - Volume 26, Issue 8, June 2005, Pages 1174-1182
نویسندگان
Quang-Anh Tran, Xing Li, Haixin Duan,