13. A method for classifying a test object, comprising the steps of: accessing a machine-readable storage device storing a training set comprising a number 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 estimating the probability of an object as being a member of each class in the training set, the estimating being performed with reference to the class assignments of the objects in the training set which are neighbors to the test object within a defined region within the training set, and classifying the test object as belonging to the class with the highest estimated probability; wherein the training set comprises objects being members of two classes, class 1 and class 2 , wherein classes 1 and 2 have numbers of objects N 1 and N 2 , respectively, in the training set, 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 the defined region and k 2 represents the number of neighbors in class 2 within the defined region.
13. A method for classifying a test object, comprising the steps of: accessing a machine-readable storage device storing a training set comprising a number 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 estimating the probability of an object as being a member of each class in the training set, the estimating being performed with reference to the class assignments of the objects in the training set which are neighbors to the test object within a defined region within the training set, and classifying the test object as belonging to the class with the highest estimated probability; wherein the training set comprises objects being members of two classes, class 1 and class 2 , wherein classes 1 and 2 have numbers of objects N 1 and N 2 , respectively, in the training set, 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 the defined region and k 2 represents the number of neighbors in class 2 within the defined region. 18. The method of claim 13 , wherein the defined region comprises a set of k objects within the training set which are the k closest objects to the test instance.