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Additional info for Applications on Advanced Architecture Computers [appl math]

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For that reason the associator is said to be optimal. The associator can learn more than one input-output, or if you will, stimulus-response (S-R) pair. Presented with a teaching input {~, Yk)' k = l, 2, ... , y,,} pairs are orthogonal to one another. If they are not orthogonal, ·the learning of one pair interferes with the learning of

In the second layer represents a distinct object (or category). Thus h. can be regarded as an object detector, and its activation signifi~s "presence" of the object. The object layer is activated by bottom-up input from the attribute layer. The signal s = a(x) from the object layer is filtered by a transfer matrix ~ to produce an input T to the object layer given by T = Ws INDUCTIVE INFERENCE BY NEUTRAL NETWORKS 27 In component form this can be written L T. i J W .. J1 S. 1 This equation can be given a probabilistic interpretation as follows.

Vol. I, II. [4] S. Grossberg, 1987, The Adaptive Brai rl , I: Cogniti~rl' learning, reinforcement aiiCfrhythm, II~si~~, speech, language and motor control, Amsterdam, Elsevier/North-Holland. [5] D. Hestenes, 1987, "How the Brain Works, the next great scientific revolution", in C. ) Maximum Entropy and Bayesian Spectral Analysis and Estimation, Reidel, Dordrecht/Boston, p. 173-205. ------- [6] B. D. Stearns, 1985, Prentice-Hall Inc. [ 7] T. :~ ~rl Springer-Verlag, Berlin. [8] T. Sejnowski and C.

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Applications on Advanced Architecture Computers [appl math] by G. Astfalk


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