288 pp. per issue
6 x 9, illustrated
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Neural Computation

June 2008, Vol. 20, No. 6, Pages 1565-1595
(doi: 10.1162/neco.2007.04-07-508)
© 2008 Massachusetts Institute of Technology
Deterministic Neural Classification
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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.