Strict Generalization in Multilayered Perceptron Networks
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The response of a multilayered perceptron (MLP) network on points which are far away from the boundary of its training data is generally never reliable. Ideally a network should not respond to data points which lie far away from the boundary of its training data. We propose a new training scheme for MLPs as classifiers, which ensures this. Our training scheme involves training subnets for each class present in the training data. Each subnet can decide whether a data point belongs to a certain class or not. Training each subnet requires data from the class which the subnet represents along with some points outside the boundary of that class. For this purpose we propose an easy but approximate method to generate points outside the boundary of a pattern class. The trained subnets are then merged to solve the multiclass classification problem. We show through simulations that an MLP trained by our method does not respond to points which lies outside the boundary of its training sample. Also, our network can deal with overlapped classes in a better manner. In addition, this scheme enables incremental training of an MLP, i.e., the MLP can learn new knowledge without forgetting the old knowledge.