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Learning multiple correct classifications from incomplete data using weakened implicit negatives

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2 Author(s)
S. Whiting ; Dept. of Comput. Sci., Brigham Young Univ., Provo, UT, USA ; D. Ventura

Classification problems with output class overlap create problems for standard neural network approaches. We present a modification of a simple feedforward 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.

Published in:

Neural Networks, 2004. Proceedings. 2004 IEEE International Joint Conference on  (Volume:4 )

Date of Conference:

25-29 July 2004