Adaptive Learning with Binary Neurons

Details Adaptive Learning with Binary Neurons Adaptive Learning with Binary Neurons

2009-04-29

arxiv.org/abs/0904.4587v1

Computer Science

Artificial Intelligence

29 pages, 7 figures

Scientific paper

A efficient incremental learning algorithm for classification tasks, called NetLines, well adapted for both binary and real-valued input patterns is presented. It generates small compact feedforward neural networks with one hidden layer of binary units and binary output units. A convergence theorem ensures that solutions with a finite number of hidden units exist for both binary and real-valued input patterns. An implementation for problems with more than two classes, valid for any binary classifier, is proposed. The generalization error and the size of the resulting networks are compared to the best published results on well-known classification benchmarks. Early stopping is shown to decrease overfitting, without improving the generalization performance.

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