کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5773551 1413508 2017 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Scale-invariant learning and convolutional networks
ترجمه فارسی عنوان
یادگیری مقیاس غیر مجرد و شبکه های کانولوشن
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه ریاضیات آنالیز ریاضی
چکیده انگلیسی
Multinomial logistic regression and other classification schemes used in conjunction with convolutional networks (convnets) were designed largely before the rise of the now standard coupling with convnets, stochastic gradient descent, and backpropagation. In the specific application to supervised learning for convnets, a simple scale-invariant classification stage is more robust than multinomial logistic regression, appears to result in somewhat lower errors on several standard test sets, has similar computational costs, and features precise control over the actual rate of learning. “Scale-invariant” means that multiplying the input values by any nonzero real number leaves the output unchanged.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied and Computational Harmonic Analysis - Volume 42, Issue 1, January 2017, Pages 154-166
نویسندگان
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