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
• 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.
Approach
• Remember which inputs got sent to which network
• After each LVQ cycle, train the backprop network for a number of cycles
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
• Many more parameters
• More epochs
• Worse Error
• Works Better for some cases
Distance
• Used inner product
• Data may not have any reason for being classified that way.
• No good distance measure for arbitrary data