Abstract: A learning model is initiated during start-up learning to activate operation of a decision system. During operation of the decision system, data is qualified for use in online learning. Online learning allows a system to adapt or learn application dependent parameters to optimize or maintain its performance during normal operation. Methods for qualifying data for use in online learning include thresholding of features, restriction of score space for qualified objects, and using a different source of information than is used in the decision process. Clustering methods are used to improve the quality of the learning model. Using the cumulative distribution function to compare two distributions and produce a measure of similarity derives a metric for learning maturity.

Abstract: A learning model is initiated during start-up learning to activate operation of a decision system. During operation of the decision system, data is qualified for use in online learning. Online learning allows a system to adapt or learn application dependent parameters to optimize or maintain its performance during normal operation. Methods for qualifying data for use in online learning include thresholding of features, restriction of score space for qualified objects, and using a different source of information than is used in the decision process. Clustering methods are used to improve the quality of the learning model. Using the cumulative distribution function to compare two distributions and produce a measure of similarity derives a metric for learning maturity.