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Incremental Neural Network Architecture

What is it about?

It is an Incremental neural architecture for supervised learning. It uses parallelly connected multi-layer perceptron network to do the same. It include a monitor perceptron to differentiate between new class (unseen class) data to seen data(already learnt). If data belongs to new class data then network extend itself to learn new class.

Why is it important?

There are many incremental architecture already exist but most of them work in two modes one is training mode and other is testing mode. If it is in training mode then it can accommodate data into it but if it is in testing mode then they classify new data into one of the existing classes which is not happening in case of INNAMP. Also it provide more tighter boundaries then other.

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The following have contributed to this page:
Sharad Gupta and Sudip Sanyal
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