Learning type decision support system

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395 11, 395 61, 395 68, G06F 1518

Patent

active

051757974

ABSTRACT:
A method and apparatus of a learning type decision support system are provided for improved acquisition of a priori knowledge from the system object and expression of non-linear structures in the object. The system is comprised of a learning module and an executing module for outputting advice and process manipulate command values to system users by receiving input data comprising on-line data and file data. The learning module comprises (1) a symbolized dictionary unit for generating a symbolized dictionary for giving a symbolic name in accordance with a pattern comprised of a combination of the values of the input data by giving examples of a pair of the pattern and the symbolic name; (2) a preprocessing unit for transforming at least a portion of the input data into the symbolic name with reference to the symbolic dictionary generated; and, (3) a model generating unit for determining an unknown parameter contained in a predetermined skeleton model to transform the preprocessed results into desired advice and manipulation command values. The executing module arithmetically processes the input data to output the advice and the manipulation command values by using the symbolized dictionary generated by the learning module and the generated model.

REFERENCES:
patent: 4979126 (1990-12-01), Pao et al.
Stephen I. Gallant, "Connectionist Expert Systems", Communications of the ACM, vol. 31, No. 2, Feb. 1988, pp. 152-169.
Richard C. Lippmann, "An Introduction to Computing with Neural Nets", IEEE ASSP Magazine, Apr. 1987, pp. 4"22.
H. Akaike, "Information Theory and an Extension of the Maximum Likelihood Principle", 2nd International Symposium on Information Theory, 1973, pp. 267-281.

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