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T h e most commonly made 28 R. 0. 45) g w = (Wi x ) ci + 9 where w iis the ith weight vector, (wi , x) is the inner product of w iand x, and ci is a constant for the ith class (Nilsson, 1965). A vector x is classified by forming these rn linear functions, and by assigning x to the category corresponding to the largest discriminant function. 48) c assigning x to w1 if g ( x ) > 0 and to augmented vectors a and y by a = x) w2 if g ( x ) I:[ < 0. 50) we can write g(x) in the homogeneous form ‘The problem of designing such a classifier is the problem of finding an (augmented) weight vector a from a set of sample patterns.

544-549 (1965). , Learning without a teacher. IEEE Trans. Info. Theory 12, No. 2, pp. 223-230 (1966). Tou, J. T. and Heydorn, R. , Some approaches to optimum feature extraction. In “Computer and Information Sciences” (J. T. ) pp. 57-89. Academic Press, New York, 1967. Widrow, B. and Hoff, M. , Adaptive switching circuits. Report No. 1553-1. Stanford Electron. , June 1960. This page intentionally left blank K. S. Fu STATISTICAL. PATTERN RECOGNITION I, Statistical Pattern Recognition Systems and Bayes Classifiers A pattern recognition system, in general, consists of two parts, namely, feature extractor and classifier.

Decision making in markov chains applied to the problem of pattern recognition. IEEE Trans. Info. 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.

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