کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4951216 | 1441194 | 2017 | 24 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Lp-norm Sauer-Shelah lemma for margin multi-category classifiers
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
پیش نمایش صفحه اول مقاله

چکیده انگلیسی
In the framework of agnostic learning, one of the main open problems of the theory of multi-category pattern classification is the characterization of the way the complexity varies with the number C of categories. More precisely, if the classifier is characterized only through minimal learnability hypotheses, then the optimal dependency on C that an upper bound on the probability of error should exhibit is unknown. We consider margin classifiers. They are based on classes of vector-valued functions with one component function per category, and the classes of component functions are uniform Glivenko-Cantelli classes. For these classifiers, an Lp-norm Sauer-Shelah lemma is established. It is then used to derive guaranteed risks in the Lâ and L2-norms. These bounds improve over the state-of-the-art ones with respect to their dependency on C, which is sublinear.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Computer and System Sciences - Volume 89, November 2017, Pages 450-473
Journal: Journal of Computer and System Sciences - Volume 89, November 2017, Pages 450-473
نویسندگان
Yann Guermeur,