کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6862999 | 1439401 | 2018 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Non-monotonic convergence of online learning algorithms for perceptrons with noisy teacher
ترجمه فارسی عنوان
یکپارچه سازی غیر همگرا از الگوریتم های یادگیری آنلاین برای پیشگامان با معلم پر سر و صدا
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
چکیده انگلیسی
Learning curves of simple perceptron were derived here. The learning curve of the perceptron learning with noisy teacher was shown to be non-monotonic, which has never appeared even though the learning curves have been analyzed for half a century. In this paper, we showed how this phenomenon occurs by analyzing the asymptotic property of the perceptron learning using a method in systems science, that is, calculating the eigenvalues of the system matrix and the corresponding eigenvectors. We also analyzed the AdaTron learning and the Hebbian learning in the same way and found that the learning curve of the AdaTron learning is non-monotonic whereas that of the Hebbian learning is monotonic.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 102, June 2018, Pages 21-26
Journal: Neural Networks - Volume 102, June 2018, Pages 21-26
نویسندگان
Kazushi Ikeda, Arata Honda, Hiroaki Hanzawa, Seiji Miyoshi,