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