neural network, learning problems, implicit negatives, classification
Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feed-forward neural network that is capable of learning problems with output overlap, including problems exhibiting hierarchical class structures in the output. Our method of applying weakened implicit negatives to address overlap and ambiguity allows the algorithm to learn a large portion of the hierarchical structure from very incomplete data. Our results show an improvement of approximately 58% over a standard backpropagation network on the hierarchical problem.
Original Publication Citation
Stephen Whiting and Dan Ventura, "Learning Multiple Correct Classifications from Incomplete Data using Weakened Implicit Negatives", Proceedings of the International Joint Conference on Neural Networks, pp. 2953-2958, July 24.
BYU ScholarsArchive Citation
Ventura, Dan A. and Whiting, Stephen, "Learning Multiple Correct Classifications from Incomplete Data using Weakened Implicit Negatives" (2004). All Faculty Publications. 433.
Physical and Mathematical Sciences
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