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Leibniz, Gottfried
 
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Lubbock, John
 



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(C) Copyright 2008-2019
Dominic John Repici
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Local Minimum

 
Specifically, with regard to neural networks, it is a state that a learning neural network sometimes gets into, where the weight adjustments for one or more training patterns simply offset the adjustments performed for a previously trained pattern. The previously trained pattern is not in its ideal desired output mapping, but is stuck in a less than ideal "local" response mapping, referred to as a local minimum. This state can sometimes be avoided by randomly jogging the connection weights (weight jogging). The underlying concept of a local minimum is often spoken of in the plural: local minima.

It is generally asserted that lower is better. That is, lower represents the more correct solution or the more desired, or ideal, state. This is leaving a lot of loose ends, of course, since adaptive systems tend to get very intertwined with philosophically unsettled issues when you begin to consider things like correctness.




Graph showing local minimums and a global minimum



Graph showing local minimums and a global minimum



Graph showing local minimums and a global minimum



Also: Weight Jogging        

 
 


































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