backpropagation, learning algorithms, softprop, generalization, lazy training
Multi-layer backpropagation, like many learning algorithms that can create complex decision surfaces, is prone to overfitting. Softprop is a novel learning approach presented here that is reminiscent of the softmax explore-exploit Q-learning search heuristic It fits the problem while delaying settling into error minima to achieve better generalization and more robust learning. This is accomplished by blending standard SSE optimization with lazy training, a new objective function well suited to learning classification tasks, to form a more stable learning model. Over several machine learning data sets, softprop reduces classification error by 17.1 percent and the variance in results by 38.6 percent over standard SSE minimization.
Original Publication Citation
Rimer, M., and Martinez, T. R., "Softprop: Softmax Neural Network Backpropagation Learning", Proceedings of the IEEE International Joint Conference on Neural Networks IJCNN'4, pp. 979-984, 24.
BYU ScholarsArchive Citation
Martinez, Tony R. and Rimer, Michael E., "Softprop: Softmax Neural Network Backpropagation Learning" (2004). All Faculty Publications. 1031.
Physical and Mathematical Sciences
© 2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright Use Information