IBNS Technical Report 2006 - 02 March 2006 Learning class regions by sphere covering
نویسندگان
چکیده
The Reduced Coulomb Energy (RCE) network is a supervised learning algorithm that learns pattern categories by representing each class region as a set of prototype regions. This method has been proven to be effective in resolving pattern classes separated by nonlinear boundaries. However, the RCE network depends on user-specified parameters which are computationally expensive to optimize. In addition, the network can create prototype regions near the class boundaries because of its stochastic nature. In this paper, we present two simple polynomial-time algorithms that create the prototype regions based on the criteria of minimizing the local probability of error and the total number of regions created. The new algorithms disfavor regions that are centered near the class boundaries because they have a higher probability of error. Compared to the RCE network, the algorithms that we present are simple to implement, do not have parameters to optimize, and achieve better or comparable results on both artificial and real-world datasets.
منابع مشابه
AFRL - IF - RS - TR - 2006 - 103 Final Technical Report March 2006 CYBER EARLY WARNING SYSTEM
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