The outcome advisor®

摘要

A new classification method called The Outcome Advisor® (OA) is presented which is an outgrowth of statistical pattern recognition and the Patrick-Fischer Generalized K-nearest Neighbor Decision Rule. Involved are new definitions of relative frequency and correlation. Training examples are store and processing begins once findings (a focus) are presented. An almost unlimited number of inferences can be made as an inference system and any feature can be used to define categories as a classification system. Implementable as a new neural net structure which is distribution free, multi-dimensional dependencies in the feature space for each category are learned utilizing a new definition of relative frequency. The new method may help explain how certain neural net structures may be estimating multidimensional dependencies. The OA has been trained and tested on established data bases and has improved performance as measured by experimental probability of error.