Adaptive versions of the Ho-Kashyap learning algorithm

Mohamad H. Hassoun

doi:10.1117/12.49652

11 November 1991 Adaptive versions of the Ho-Kashyap learning algorithm

Mohamad H. Hassoun

Proceedings Volume 1558, Wave Propagation and Scattering in Varied Media II; (1991) https://doi.org/10.1117/12.49652
Event: San Diego, '91, 1991, San Diego, CA, United States

Abstract

Three adaptive version of the Ho-Kashyap perceptron training algorithm are derived based on gradient descent strategies. These adaptive Ho-Kashyap (AHK) training rules are comparable in their complexity to the LMS and perceptron training rules and are capable of adaptively forming linear discriminant surfaces which guarantee linear separability and of positioning such surfaces for maximal classification robustness. In particular, a derived version called AHK II is capable of adaptively identifying critical input vectors lying close to class boundaries in linearly separable problems. We extend this algorithm as AHK III, which adds the capability of fast convergence to linear discriminant surfaces that are 'good' approximations for nonlinearly separable problems.

Citation Download Citation

Mohamad H. Hassoun "Adaptive versions of the Ho-Kashyap learning algorithm", Proc. SPIE 1558, Wave Propagation and Scattering in Varied Media II, (11 November 1991); https://doi.org/10.1117/12.49652

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