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Alternate Learning as a more stable method for learning modular

Kuiken, M. (2000) Alternate Learning as a more stable method for learning modular. Master's Thesis / Essay, Computing Science.

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When using the standard error backpropagation algorithm, modular neural networks are often very difficult to train. Because of change in the structure of a modular network (there is no longer full connectivity between adjacent layers) compared to that of a 'normal' neural network, one should not be surprised to see an instabile learning process. Techniques to still be able to train modular structures tend to be 'artificial' solutions, like for example not training certain submodules for a period of time during the training process or simply by using a very low learning rate (which has a stabilizing effect). However one needs to do research for every problem (modular network); which submodule must when be fixed and for how long etc. One would rather have a ready-made solution which brings the trainings process, without having knowledge about the modular network, to a successful ending. Because of the loss of full connectivity between the layers together with the fact that a generated error is propagated back throughout the whole network and therefore adjusting all modules, it is thought there is too much correction in the modular network resulting in even bigger errors. The suggested learning method (Alternate Learning), being discussed in this paper, solves this problem by adapting only selected modules instead of the whole modular network.Two experiments have been done to test Alternate Learning. During each of these experiments three different selection procedures have been used to select submodules for adapting. These Alternate Learning experiments have been compared with results gained from training according to the 'standard' method (training all submodules). Performance criteria consist out of stability during a run, stability among several runs (robustness) and the absolute error. Although selection procedures among performed differently, the overall results showed that when using the above mentioned performance criteria Alternate Learning performed better. These experiments showed that Alternate Learning is a promising alternative and that therefore more research has to be done.

Item Type: Thesis (Master's Thesis / Essay)
Degree programme: Computing Science
Thesis type: Master's Thesis / Essay
Language: English
Date Deposited: 15 Feb 2018 07:29
Last Modified: 15 Feb 2018 07:29

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