Lvq selection of a backprop network
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LVQ Selection of A BackProp Network. Problem Statement. Use a Learning Vector Quantization network to split up the data set and then feed each smaller input set to a backprop network. Compare the results to a single larger backprop network. Approach.

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Problem statement
Problem Statement

  • Use a Learning Vector Quantization network to split up the data set and then feed each smaller input set to a backprop network. Compare the results to a single larger backprop network


Approach
Approach

  • On each cycle, select the closest weight in the LVQ network.

  • Move the weight towards the input if the network it represents produces the correct output.

  • If it doesn’t, find some weight vector that does.


Approach1
Approach

  • Remember which inputs got sent to which network

  • After each LVQ cycle, train the backprop network for a number of cycles


Implementation details
Implementation Details

  • LVQ is very similar to a standard LVQ network, except it remembers how things were classified

  • At the end of each cycle it trains the BP networks

  • Each BP network is stored in a separate file


Results
Results

  • Many more parameters

  • More epochs

  • Worse Error

  • Works Better for some cases


Distance
Distance

  • Used inner product

  • Data may not have any reason for being classified that way.

  • No good distance measure for arbitrary data