Abstract
This paper presents an algorithm for online adaptation of the kernel width parameter in information theoretic cost functions used for adaptive system training. Training algorithms which optimize information theoretic quantities like entropy involve choosing a kernel size for their sample estimators. The kernel size essentially dictates the nature of the performance surface of the cost function over which the system parameters adapt. This, in turn, governs factors like speed of adaptation and presence of local minima. We show results of using the Minimum Error Entropy (MEE) criterion with the proposed adaptive kernel algorithm for training a time delay neural network. Our simulations show that having an adaptive kernel width results in faster convergence of parameters as compared to having fixed values.
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 callDisclaimer: 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.
