A class of asymptotically stable algorithms for learning-rate adaptation

Rüger, Stefan (1998). A class of asymptotically stable algorithms for learning-rate adaptation. Algorithmica, 22(1-2) pp. 198–210.

DOI: https://doi.org/10.1007/PL00013830

Abstract

A stability criterion for learning is given. In the case of learning-rate adaptation of backpropagation, a class of asymptotically stable algorithms is presented and studied, including a convergence proof. Simulations demonstrate relevance and limitations.

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About

• Item ORO ID
• 11955
• Item Type
• Journal Item
• ISSN
• 1432-0541
• Keywords
• convex optimization; neural learning; learning-rate adaptation; line search; stability criterion
• Academic Unit or School
• Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
  Faculty of Science, Technology, Engineering and Mathematics (STEM)
• Copyright Holders
• © 1998 Springer-Verlag
• Depositing User
• Users 8580 not found.