6.2. Class-Based Modular Networks
An important goal of pattern recognition is determining to which class an input pattern best belongs. Therefore, it is natural to consider class-level modules as the basic partitioning units, where each module specializes in distinguishing its own class from the others. Consequently, the number of hidden nodes designated to each class tends to be very small. The class-level modules are adopted by the OCON network. In contrast to expert-level partitioning, this OCON structure facilitates a global (or mutual) supervised training scheme. In global interclass supervised learning, any dispute over a pattern region by (two or more) competing classes can be effectively resolved by resorting to the teacher's guidance. ...
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