Abstract
Simple examples clearly demonstrate that highly consistent data lead to solution nonattainability, in neural networks utilizing a logistics sigmoid function. Solution attainability requires a high degree of inconsistency. Bounds are obtained on the optimal value of the mean-square error of a one-layer neural network, in terms of the minimum number of misclassifications obtained from three linear classification problems, and conditions are given that imply solution attainability and nonattainability
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
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.