10. A method as in claim 1 , wherein the degree of mis-classification is determined by the function 
 d ( x ) =P ( C 1 |x ) −P ( C 0 |x ) where C0 is a first class, C1 is a second class, and P(C0|x)is a first discriminant function and P(C1|x) is a second discriminant function representing Bayesian posterior probabilities for a sample vector x.
1. A method for classifying and sorting image input data in an image data stream, wherein the image input data includes data representative of at least one physical object, comprising: a) sampling the image input data with an input control; b) comparing one or more classes of the sampled image input data with initially preset data classifications that constitute a baseline for determining mis-classification of data patterns; c) determining a degree of mis-classification of the image input data and assigning a probability proportional to the degree of mis-classification thereto as a criterion for storing the input in a resampling buffer; d) entering data patterns causing mis-classification in a resampling buffer with a probability value proportional to the degree of mis-classification; e) comparing the data patterns to a ground truth source and aligning the data patterns with their associated data pattern labels employing the same mis-classification probability as applied to the resampling buffer to form a set of training data; and f) updating an adaptive classifier to correlate with the training data; and g) repeating steps a)-f) until a sufficient degree of data classification optimization is realized, thereby transforming the image input data into an optimized image output data set with an improved representation of said at least one physical object.