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Extra info for Adaptive, Learning and Pattern Recognition Systems: Theory and Applications
The values of the features are sent to a classifier, which assigns the input object to one of a fixed number of categories. This entire process can be viewed mathematically as a mapping. Each input can be viewed as a point z in object space. The transducer maps each z into a point y in representation space. T h e feature extractor maps each y into a point x in feature space. Finally, the classifier maps each x into a discrete-valued scalar d in decision space, where d = di if the classifier assigns x to the ith category.
I n this section, a more general sequential pattern classifier is considered. T h e classifier so designed has the additional capability of selecting the best feature for the next measurement. I n other words, in the process of sequential decisions, if the decision is to continue taking an additional measurement, it also, in the meantime, selects the best feature for the next measurement. ,fN) be the set of N features extracted by the feature extractor in their natural order. , N be a particular sequence of n features measured by the classifier at the nth stage of the sequential recognition process.
Meteorological Monographs 4 , No. 25 (1962). Nagy, G. and Shelton, G. , Self-corrective character recognition system. IEEE Trans. Info. Theory 12, No. 2, pp. 215-222 (1966). , State of the art in pattern recognition. Proc. IEEE 5 6 , No. 5, pp. 836-862 (1968). Nilsson, N. ” McGraw-Hill, New York, 1965. , 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.