c ○ 2008 Yang LiINCREMENTAL TRAINING AND GROWTH OF ARTIFICIAL NEURAL NETWORKS BY
BibTeX
@MISC{Li_c○,
author = {Yang Li},
title = {c ○ 2008 Yang LiINCREMENTAL TRAINING AND GROWTH OF ARTIFICIAL NEURAL NETWORKS BY},
year = {}
}
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Abstract
Training of automatic pattern recognition or function regression systems has been investigated for decades, and it is fairly well understood that the usage of limited amounts of empirical data in the training of such systems necessarily leads to generalization difficulties. The focus of this work is to investigate the generalization issues associated with a particular class of estimators- artificial neural networks- and formulate a novel method to improve the trade-off between performance and generalizability when it comes to training with a limited amount of empirical data. The improvement comes from an effective utilization of prior knowledge: if a network can be trained on a large training corpus sharing certain characteristics with the data from the task at hand, then the network can be “grown ” to be adapted to solve the current task. The network carries the structure obtained from its training on the large dataset over to the smaller dataset; if there are similarities in the structure, this preservation of structure across applications expedites training and ensures lower variability. The thesis
