We consider the samples x(i) belonging to one of the two non-ov-rlapping classes, "1 and uO9 whic... more We consider the samples x(i) belonging to one of the two non-ov-rlapping classes, "1 and uO9 which possess a separating function f(x). membership of pattern x(i) is represented by the variable z(i) which can assume only one of two values, f-1 , or z(i) = [sgn f(x(i))]y(i) where n(i) is the measurement noise and E(T) is known. the training samples may be erroneous. pairs fx(i), z(i)I9 i=l, 2, ., we will obtain an optimal approximation to the separating function f (x).
Abstracf-First-and second-order stochastic gradient algorithms are developed for suitably approxi... more Abstracf-First-and second-order stochastic gradient algorithms are developed for suitably approximating the unknown density and distribution functions of a random vector from a sequence of independent samples. The mean-square-error criterion and the integral-square-error criterion are used in the approximations. The rates of convergence and the approximation error are also evaluated.
We consider the samples x(i) belonging to one of the two non-ov-rlapping classes, "1 and uO9 whic... more We consider the samples x(i) belonging to one of the two non-ov-rlapping classes, "1 and uO9 which possess a separating function f(x). membership of pattern x(i) is represented by the variable z(i) which can assume only one of two values, f-1 , or z(i) = [sgn f(x(i))]y(i) where n(i) is the measurement noise and E(T) is known. the training samples may be erroneous. pairs fx(i), z(i)I9 i=l, 2, ., we will obtain an optimal approximation to the separating function f (x).
Abstracf-First-and second-order stochastic gradient algorithms are developed for suitably approxi... more Abstracf-First-and second-order stochastic gradient algorithms are developed for suitably approximating the unknown density and distribution functions of a random vector from a sequence of independent samples. The mean-square-error criterion and the integral-square-error criterion are used in the approximations. The rates of convergence and the approximation error are also evaluated.
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Papers by Colin Blaydon