A Model for Optimizing the Effectiveness of Man-Machine Decision Making in a Pattern Recognition System

摘要

Typical pattern-recognition processes can be separated into several components, some of which may be more readily automated than others. Humans seem to be particularly suited for the earlier parts of processing, such as delineating a part of the image to be recognized as a single object and adaptively selecting an effective feature space for a given task context. On the other hand, optimal decision processes--which give due weight to prior probabilities, take into account the differential costs of errors, and utilize efficient statistical classification procedures -- can now be automated on the basis of an already well developed body of knowledge. They may be better handled by machines than by men. Recent work suggests that a reasonable model for human pattern recognition can usefully incorporate processes such as mapping an unknown pattern into a subjective feature space and classifying it on the basis of its location in that space. In terms of this model and of the above considerations, the best point at which to tap into the human pattern recognition process may well be at the feature-space level rather than at the classification level. The paper proposes a method for relating this subjective feature to an objective feature space of a machine so that a human could serve as preprocesser and feature analyzer while the machine could carry out the statistical classification processes. (Author)