کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8941844 | 1645039 | 2018 | 23 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Localization of VC classes: Beyond local Rademacher complexities
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
In statistical learning the excess risk of empirical risk minimization (ERM) is controlled by (COMPn(F)n)α, where n is a size of a learning sample, COMPn(F) is a complexity term associated with a given class F and αâ[12,1] interpolates between slow and fast learning rates. In this paper we introduce an alternative localization approach for binary classification that leads to a novel complexity measure: fixed points of the local empirical entropy. We show that this complexity measure gives a tight control over COMPn(F) in the upper bounds under bounded noise. Our results are accompanied by a minimax lower bound that involves the same quantity. In particular, we practically answer the question of optimality of ERM under bounded noise for general VC classes.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Theoretical Computer Science - Volume 742, 19 September 2018, Pages 27-49
Journal: Theoretical Computer Science - Volume 742, 19 September 2018, Pages 27-49
نویسندگان
N. Zhivotovskiy, S. Hanneke,