Abstract
An information theory based method for the training of perceptrons is presented. Our technique guarantees an errorless learning process for learnable mappings with just a minimum amount of examples. The only requirement is that the transfer function must possess an inverse. Some illustrative results are presented. The method can be considered to yield another tool for feed-forward training.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
