Deterministic neural classification

Neural Computation
Kar-Ann Toh

Abstract

This letter presents a minimum classification error learning formulation for a single-layer feedforward network (SLFN). By approximating the nonlinear counting step function using a quadratic function, the classification error rate is shown to be deterministically solvable. Essentially the derived solution is related to an existing weighted least-squares method with class-specific weights set according to the size of data set. By considering the class-specific weights as adjustable parameters, the learning formulation extends the classification robustness of the SLFN without sacrificing its intrinsic advantage of being a closed-form algorithm. While the method is applicable to other linear formulations, our empirical results indicate SLFN's effectiveness on classification generalization.

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Citations

Dec 23, 2014·IEEE Transactions on Neural Networks and Learning Systems·Vijay Manikandan JanakiramanDennis Assanis
Oct 11, 2011·IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society·Guang-Bin HuangRui Zhang
Feb 6, 2020·Chaos·Shraddha M NaikVenkatanareshbabu Kuppili

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