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LVQ Selection of A BackProp Network

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.

damian-clements
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LVQ Selection of A BackProp Network

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  1. LVQ Selection of A BackProp Network

  2. 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

  3. 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.

  4. Approach • Remember which inputs got sent to which network • After each LVQ cycle, train the backprop network for a number of cycles

  5. 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

  6. Results • Many more parameters • More epochs • Worse Error • Works Better for some cases

  7. Distance • Used inner product • Data may not have any reason for being classified that way. • No good distance measure for arbitrary data

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