1. 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; 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 each of the classes in the training set, wherein the probabilistic classifier estimates the probabilities 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; and wherein the training set comprises objects being members of two classes, class 1 and class 2 , wherein classes 1 and 2 in the training set have N 1 and N 2 numbers of objects respectively, in the training set and wherein the computer system is programmed to determine the probability P(class 1 ) of the object being a member of class 1 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 the defined region and k 2 represents the number of neighbors in class 2 within the defined region.