Abstract
The present paper is a continuation of our earlier work on the Generalized Guard-Zones Algorithm (GGA) for self-supervised parameter learning. A theory for automatic selection of guard zone parameter (threshold) for practical implementation of the GGA has recently been reported based on a probabilistic model involving mislabelling and large sample theory. Some applications of this theoretical evaluation have been described here for demonstrating its efficacy to real life pattern recognition problems, such as speech recognition, and terrain classification based on LANDSAT-V data. The experimental results confirm all the the theoretical features highlighted in a previous paper when a Bayes classifier is used for labelling a sample.
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.
