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Extra resources for Adaptive, Learning and Pattern Recognition Systems: Theory and Applications
13, No. 5 , pp. 586-593 (1964). Marill, T. and Green, D. , On the effectiveness of receptors in recognition systems. IEEE Trans. Info. Theory 9, No. 1, pp. 11-17 (1963). Marill, T. , CYCLOPS-1: a second-generation recognition system. Proc. FJCC, Las Vegas, Nevada, 1963. Spartan, Washington, D. , 1963. Miller, R. , Statistical prediction by discriminant analysis. Meteorological Monographs 4 , No. 25 (1962). Nagy, G. and Shelton, G. , Self-corrective character recognition system. IEEE Trans. Info.
36) is obtained from Eq. 35). , x N - , ) is obtained from Eq. 36). At second stage, 48 K . S. FU in which R,(xl, x 2 , xg) is obtained from the third stage. At first stage, in which R2(xl , x 2 ) is obtained from Eq. 38). , N . I n addition, the optimal stopping rule is also determined at each stage. T h a t is, if the risk of stopping is less than the expected risk of continuing for a given history of feature measurements, the sequential process is terminated. 34). V. Backward Sequential Procedure for Both Feature Selection and Pattern Classification I n Section IV, the dynamic programming procedure has been applied to pattern classification problems without considering the ordering of feature measurements.
T h e method of modifying the sequential probability ratio test as described in Section I11 is certainly one approach for solving this class of decision problems. However, the optimality of the original decision procedure is frequently sacrificed, especially in the multiple decision case ( m > 2). T h e optimal Bayes’ sequential decision procedure which minimizes the expected risk including the cost of observations is essentially a backward procedure (Blackwell and Girshick, 1954). I t is intended to show in this section that, as an alternative approach to the modified sequential probability ratio test, the dynamic programming (Bellman, 1957; Bellman et.