24. A method of classification of a test instance comprising the steps of: creating and storing in a machine-readable memory a training set comprising a plurality of objects, the objects being assigned a class label, the objects being members of either class 1 or a class 2 ; obtaining a test instance to be classified; classifying the test instance with a computer system configured as a probabilistic classifier by estimating the probability that the test instance is a member of classes 1 and 2 , wherein the probability P(class 1 ) of the test instance being a member of Class 1 is estimated according to the relation: P ⁡ ( class ⁢ ⁢ 1 ) = k 1 + 1 k 1 + k 2 + 2 · F 1 2 ⁡ ( 1 , k 2 + 1 ; k 1 + k 2 + 3 ; 1 - N 1 N 2 ) where 2 F 1 ( 1 , k 2 +1; k 1 +k 2 +3; 1−N1/N2) is a calculation of the hypergeometric function 2 F 1 and where k 1 represents the number of neighbors of class 1 within a defined region in the training set and k 2 represents the number of neighbors in class 2 within the defined region, N 1 is the number of objects in class 1 in the training set, and N 2 is the number of objects in class 2 in the training set.
24. A method of classification of a test instance comprising the steps of: creating and storing in a machine-readable memory a training set comprising a plurality of objects, the objects being assigned a class label, the objects being members of either class 1 or a class 2 ; obtaining a test instance to be classified; classifying the test instance with a computer system configured as a probabilistic classifier by estimating the probability that the test instance is a member of classes 1 and 2 , wherein the probability P(class 1 ) of the test instance being a member of Class 1 is estimated according to the relation: P ⁡ ( class ⁢ ⁢ 1 ) = k 1 + 1 k 1 + k 2 + 2 · F 1 2 ⁡ ( 1 , k 2 + 1 ; k 1 + k 2 + 3 ; 1 - N 1 N 2 ) where 2 F 1 ( 1 , k 2 +1; k 1 +k 2 +3; 1−N1/N2) is a calculation of the hypergeometric function 2 F 1 and where k 1 represents the number of neighbors of class 1 within a defined region in the training set and k 2 represents the number of neighbors in class 2 within the defined region, N 1 is the number of objects in class 1 in the training set, and N 2 is the number of objects in class 2 in the training set. 27. The method of claim 24 , further comprising the steps of estimating the probability that the test instance is a member of classes 1 and 2 , and comparing the estimates of probabilities for each of the classes to determine if the difference of estimated probability between the class having the highest probability estimate and the class having the next highest probability estimate is greater than a user-supplied parameter.