By Mendel

ISBN-10: 0124907504

ISBN-13: 9780124907508

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4, pp. 536-551 (1967). Roberts, L. , Machine perception of three-dimensional solids. In “Optical and ElectroOptical Information Processing” (J. T. ) pp. 159-197. , 1965. , The perceptron: a perceiving and recognizing automaton. Report No. 85-460-1. Cornell Aeronautical Laboratory, Buffalo, New York, 1957. Sebestyen, G. , Pattern recognition by an adaptive process of sample set construction. I R E Trans. Info. Theory 8, No. 5 , pp. S82-S91 (1962). , A note on the iterative application of Bayes’ rule.

Theory 13, No. 4, pp. 536-551 (1967). Roberts, L. , Machine perception of three-dimensional solids. In “Optical and ElectroOptical Information Processing” (J. T. ) pp. 159-197. , 1965. , The perceptron: a perceiving and recognizing automaton. Report No. 85-460-1. Cornell Aeronautical Laboratory, Buffalo, New York, 1957. Sebestyen, G. , Pattern recognition by an adaptive process of sample set construction. I R E Trans. Info. Theory 8, No. 5 , pp. S82-S91 (1962). , A note on the iterative application of Bayes’ rule.

30 R. 0. 55) Thus, if hand-printed B’s and 8’s are selected randomly, the probability density for the straightness ratio is a mixture of two normal densities. If we use labeled samples to estimate pB and paroughly to begin with, then we may have a fairly good initial idea of 8 , which we can represent by a density p ( 8 ) . If we now start receiving unlabeled samples, we can use the recursive Bayes approach to get a better and better idea of 8. From this viewpoint, the distinction between supervised and unsupervised learning disappears, and we see that under appropriate assumptions learning without a teacher is certainly possible.

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