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Classifying visual inputs

I had a look through your code and I've modified Rodney's STVM accordingly. I do remember seeing a video a long long time ago where (I think) Grossberg described his ART algorithm. It was a long boring highly mathematical lecture and I never payed much attention at the time. But reading your paper it seemed like the classification version of ART would be a neat solution to classifying the robots visual input. The expanding and contracting tollerances (receptive fields) mean that the robot can produce fine classifications for objects which are more interesting or more frequently observed, and fairly broad classifications over areas of the feature space which aren't very interesting (such as Grossberg lectures).

I don't know exactly how you did it but I just wrote an algorithm such that each "neuron" tries maintain a constant number of neighbours within its receptive field. If there are not enough neighbours the field expands. If there are too many neighbours the field contracts.

- Bob

136 posts.
Sunday 02 June, 17:41
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Good idea

Bob, Trying to maintain a constant number of neighbours per neuron sounds very reasonable. One of my research aims was to show that a model of curiosity could be built with standard components so I just implemented the algorithm as published -- reducing the receptive field by a fixed percentage (eg. 10%) as needed. I'd be interested to know how you get on with your variation. -RoB-

47 posts.
Monday 03 June, 16:18
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