Patent ID: 7333961

Claim:
An artificial vision system training method, including the steps: with an artificial vision system, sampling a set of feature sample vectors; with the artificial vision system, sampling a set of response sample scalars; in the artificial vision system, forming a feature matrix including feature sample vectors and a corresponding response sample vector including response sample scalars; in the artificial vision system, determining a linkage vector linking said feature matrix to said response sample vector, characterized by an iterative linkage vector determining method including the steps: determining a response sample vector error estimate in the response sample vector domain; transforming said response sample vector error estimate into a corresponding linkage vector error estimate in the linkage vector domain; determining a linkage vector estimate in the linkage vector domain by using said linkage vector error estimate; transforming said linkage vector estimate into a corresponding response sample vector estimate in the response sample vector domain; repeating the previous iterative steps until the response sample vector error estimate is sufficiently small so that the artificial vision system is trained; and an iteration step including: determining a response sample vector error estimate representing the difference between a current response sample vector estimate and said response sample vector; using the transpose of said feature matrix for transforming said response sample vector error estimate into said linkage vector error estimate; forming an updated linkage vector estimate by subtracting said linkage vector error estimate from a current linkage vector estimate; and using said feature matrix for transforming said updated linkage vector estimate into an updated response sample vector estimate.