Abstract
Adaptive pattern recognizers incorporate approximations to the class probability distributions that improve as a result of cumulative information derived from known inputs. A simple method is outlined in which a stored sample constitutes a set of points in observation-space at each of which the local value of a class's distribution can be estimated cumulatively as known class-members become available. The membership of the sample representing each class is modified to fill gaps and to adjust to the relative spread of the distribution. Only a minimum of computation, mostly simple counting, is required.
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.
