28. A system for classification of a test object, comprising: a machine-readable storage device storing a training set comprising a plurality of objects, each of the objects being assigned as a member of a class, collectively the objects in the training set comprising members of at least two classes, including class 1 and class 2 ; and a computer system coupled to the machine-readable storage device configured as a probabilistic classifier, the probabilistic classifier estimating the probability of the test object as being a member of a class in the training set, wherein the probabilistic classifier estimates the probability with reference to the class assignments of objects in the training set which are neighbors to the test object within a defined region within the training set; wherein classes 1 and 2 have N 1 and N 2 objects, respectively, in the training set, wherein N 1 and N 2 are not the same; and wherein there are only two classes of objects in the training set, classes 1 and 2 , and 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.
28. A system for classification of a test object, comprising: a machine-readable storage device storing a training set comprising a plurality of objects, each of the objects being assigned as a member of a class, collectively the objects in the training set comprising members of at least two classes, including class 1 and class 2 ; and a computer system coupled to the machine-readable storage device configured as a probabilistic classifier, the probabilistic classifier estimating the probability of the test object as being a member of a class in the training set, wherein the probabilistic classifier estimates the probability with reference to the class assignments of objects in the training set which are neighbors to the test object within a defined region within the training set; wherein classes 1 and 2 have N 1 and N 2 objects, respectively, in the training set, wherein N 1 and N 2 are not the same; and wherein there are only two classes of objects in the training set, classes 1 and 2 , and 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. 29. The system of claim 28 , wherein the probabilistic classifier estimates the probability of the test object as being a member of each of the classes in the training set and classifies the test object as belonging to the class with the highest estimated probability.