Systems and methods for classifying test data based on maximum margin classifier are described. In one implementation, the method includes obtaining training data having a predefined sample size, wherein the training data is composed of separable data-sets. For the training data, a Vapnik-Chervonenkis (VC) dimension for the training data is determined. For the VC dimension, an exact bound is subsequently determined. The exact bound may be minimized for obtaining the minimum VC classifier for predicting at least one class to which samples of the training data belong.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for classifying binary data, the method comprising: obtaining training data having a predefined sample size, wherein the training data is composed of separable datasets; determining an exact bound on Vapnik-Chervonenkis (VC) dimension of a hyperplane classifier for the training data, wherein the exact bound is based one or more variables defining the hyperplane; and minimizing the exact bound on the VC dimension; and based on the minimizing of the exact bound, determining the optimal values of the one or more variables defining the hyperplane; generating a binary classifier for predicting one class to which a given data sample belongs. 2. The method as claimed in claim 1, wherein the exact bound is a function of the distances of closest and furthest from amongst the training data from the hyperplane, wherein the hyperplane classifies plurality of points within the training data with zero error. 3. The method as claimed in claim 1, wherein the datasets are one of linearly separable datasets and non-linearly separable datasets. 4. The method as claimed in claim 2, wherein for the notional hyperplane depicted by the following relation: uTx+v=0, the exact bound on the VC dimension for the hyperplane classifier is a function of h, being defined by: h = Max i = 1 , 2 , …  , M   u T  x i + v  Min i = 1 , 2 , …  , M   u T  x i + v  wherein, xi, i=1,2, . . . , M depict data points within the training data. 5. The method as claimed in claim 2, wherein the function to be minimized is another function of h added to a misclassification error parameter. 6. The method as claimed in claim 4, wherein the minimizing the exact bound further comprises: reducing the linear fractional programming problem of minimizing the h to obtain a linear programming problem; by solving the linear programming problem so obtained, obtaining a decision function for classifying the test data. 7. The method as claimed in claim 6, wherein the decision function has a low VC dimension. 8. The method as claimed in claim 6, wherein the objective of, the linear programming problem includes a function of the misclassification error. 9. A system for classifying test data, the system comprising: a processor; a data classification module, wherein the data classification module is to, obtaining training data having a predefined sample size, wherein the training data is composed of separable datasets having; determining an exact bound on the Vapnik-Chervonenkis (VC) dimension of a hyperplane classifier for the training data, wherein the exact bound depends on the variables defining the said hyperplane minimizing the exact bound on the VC dimension; and based on the minimizing of the exact bound, determining the optimal values of the variables defining the hyperplane, thus generating a binary classifier for predicting one class to which a given data sample belongs. 10. The system as claimed in claim 8, wherein the data classification module for nonlinearly separable datasets in a first dimension, is to map samples of training data from the first dimension to a higher dimension using a mapping function φ. 11. The system as claimed in claim 9, wherein for a notional hyperplane depicted by the relation uTφ(x)+v=0, the data classification module is to: minimize an exact bound on the VC dimension of a hyperplane classifier wherein the said classifier separates samples that have been transformed from the input dimension to a higher dimension by means of the mapping function (φ); wherein the minimization task is achieved by solving a fractional programming problem that has been reduced to a linear programming problem. 12. The system as claimed in claim 9, where data classification module utilizes a Kernel function K, wherein, K is a function of two input vectors ‘a’ and ‘b’ with K being positive definite; and K(a,b)=φ(a)Tφ(b) with K(a,b) being an inner product of the vectors obtained by transforming vectors ‘a’ and ‘b’ into a higher dimensional space by using the mapping function φ. 13. The system as claimed in claim 8, wherein alternatively the data classification module is to further: obtain a tolerance regression parameter, for a plurality of points within the training data; obtain the value of a hypothetical function or measurement at each of said training samples derive a classification problem in which the samples of each of the two classes are determined by using the given data and the tolerance parameter define a notional hyperplane, wherein the notional hyperplane classifies the plurality of points within the derived classification problem with minimal error; and based on the notional hyperplane, generates a regressor corresponding to the plurality of points. 14. The system as claimed in claim 13, wherein for the notional hyperplane is defined by, wTx+ηy+v=0, the data classification module generates the regressor defined by, y = - 1 η  ( w T  x + b ) 15. The system as claimed in claim 14, wherein for the points forming a linearly separable dataset, the regressor is a linear regressor. 16. The system as claimed in claim 14, wherein for the points forming a nonlinearly separable dataset, the regressor is a kernel regressor. 17. The system as claimed in claim 14, wherein the regressor further includes an error parameter. 18. The method as claimed in claim 8, in which the solution of the linear programming problem yields a set of weights or co-efficients, with each weight corresponding to an input feature, attribute, or co-ordinate, and wherein the set of input features with non-zero weights constitutes a set of selected features to allow feature selection. 19. The method as claimed in claim 18, in which only the selected features are used to next compute a classifier, thus eliminating the noise or confusion introduced by features that are less discriminative. 20. The method as claimed in claim 5, in which the constraints are modified so that one of the terms of the objective function is non-essential and can be removed. 21. The method as claimed in claim 20, in which the removal of a term in the objective function removes the need to choose a hyper-parameter weighting the mis-classification error, thus simplifying the use of the said method. 22. The method as claimed in claim 17, in which the constraints are modified so that one of the terms of the objective function is non-essential and can be removed. 23. The method as claimed in claim 4, in which the Max function is, replaced by a “soft Max” function in which distance is measured as a weighted function of distances from a plurality of hyperplanes, and in which the Min, function is replaced by a “soft Min” function. 24. The system as claimed in claim 14, in which the Max function is replaced by a “soft Max” function in which distance is measured as a weighted function of distances from a plurality of hyperplanes, and in which the Min function is replaced by a “soft Min” function.