A Functional Analytic Approach to Incremental Learning in Optimally Generalizing Neural Networks
Sethu Vijayakumar and Hidemitsu Ogawa
Abstract of paper published in Proceedings of the IEEE International Conference on Neural Networks, ICNN '95, Perth, W.Australia, Nov.27-Dec.1, 1995. Awarded the Best Student Paper Award at the conference.
For a given set of training data, a method of learning for optimally
generalizing neural networks using functional analytic approach already exists. Here, we consider the case when additional training data is made available at a later stage. We devise a method of carrying out optimal learning with respect to the entire set of training data (including the newly added one) using the results of the previously learned stage. This ensures that the learning operator and the learned function can both be computed incrementally, leading to a reduced computational cost. Finally, we also provide a simplified relationship
between the newly learned function and the previous function, opening avenues
for work into selection of optimal training set.
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