Computer implemented system and method for classifying users is provided. The method includes obtaining a data set from one or more sources corresponding to one or more users; segmenting the data set into segments, each segment comprising one or more data points, wherein each data point is specific and unique to a user; identifying difficult regions from one or more segments; computing a standard deviation of distances between the data points within each of the identified difficult regions for each of the identified difficult regions; selecting a radial basis function (RBF) kernel based on the standard deviation computed for each of the identified difficult regions; generating a support vector machine model on each of the identified difficult regions based on the selected RBF kernel to obtain a hyper plane comprising support vectors and weight assigned to the vectors; and classifying the users into a category based on the hyper plane.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A computer implemented method for classifying one or more users, said computer implemented method comprising: obtaining a data set from one or more sources, wherein said data set comprises information corresponding to one or more users; segmenting said data set into one or more segments using a decision tree, each segment comprising one or more data points, wherein each data point is specific and unique to a user, and wherein said decision tree comprises one or more nodes, each node comprising at least one data point; identifying a plurality of difficult regions from said one or more segments to obtain one or more identified difficult regions, wherein each of said one or more identified difficult regions comprises a set of data points; computing a standard deviation of distances between said set of data points within each of said one or more identified difficult regions for each of said one or more identified difficult regions; selecting a radial basis function (RBF) kernel based on said standard deviation computed for each of said one or more identified difficult regions to obtain a selected RBF kernel; generating a support vector machine model on each of said one or more identified difficult regions based on said selected RBF kernel to obtain a hyper plane comprising a plurality of support vectors and weight assigned to each of said plurality of support vectors; and classifying said one or more users into at least one category based on said hyper plane. 2. The computer implemented method of claim 1, wherein said one or more difficult regions are identified based on a purity level and number of data points in at least one node of said decision tree. 3. The computer implemented method of claim 1, wherein said standard deviation is computed in accordance with an equation: K  ( X i , X ) = exp  ( -  X i - X j  2 2   σ 2 ) , wherein K(Xi,X) is a kernel function, wherein σ is a value in said RBF kernel, and where Xi and Xj are data points. 4. The computer implemented method of claim 3, wherein said hyper plane is obtained in accordance with an equation: f  ( X ) = ∑ ( i ∈ suppor   vectors }  α i  y i  K  ( X i , X ) + b , where i ranges from 1 to n, where αi is said weight assigned to each of said plurality of support vectors ‘i’, and b is the bias. 5. The computer implemented method of claim 1, wherein said one or more sources is selected from a group comprising a telecommunication industry, a financial institution, a medical institution, and a retail industry. 6. The computer implemented method of claim 1, wherein said at least one category is selected from a group comprising a credit defaulter, a health disease, and a fraudulent detection. 7. A computer implemented system for classifying one or more users, said computer implemented system comprising: a hardware processor; a memory that stores instructions and a database, wherein said database comprising a data set obtained from one or more sources, wherein said data set comprises information corresponding to one or more users, and wherein said hardware processor is configured by said instructions to execute: a segmentation module that segments said data set into one or more segments using a decision tree, each of said one or more segments comprising one or more data points, wherein each data point is specific and unique to a user, and wherein said decision tree comprises one or more nodes, each node comprising at least one data point; a difficult region identification module that identifies a plurality of difficult regions from said one or more segments to obtain one or more identified difficult regions, wherein each of said one or more identified difficult regions comprises a set of data points; a standard deviation computation module that computes a standard deviation of distances between said set of data points within each of said one or more identified difficult regions for each of said one or more identified difficult regions; a radial basis function selection module that selects a radial basis function (RBF) kernel based on said standard deviation computed for each of said one or more identified difficult regions to obtain a selected RBF kernel; a support vector machine model generation module that generates a support vector machine model on each of said one or more identified difficult regions based on said selected RBF kernel to obtain a hyper plane comprising a plurality of support vectors and weight assigned to each of said plurality of support vectors; and a classification module that classifies said one or more users into at least one category based on said hyper plane. 8. The computer implemented system of 7, wherein said one or more difficult regions are identified based on a purity level and number of data points in at least one node of said decision tree. 9. The computer implemented system of claim 7, wherein said standard deviation is computed in accordance with an equation: K  ( X i , X ) = exp  ( -  X i - X j  2 2   σ 2 ) , wherein K(Xi,X) is a kernel function, where σ is a value in said RBF kernel, and where Xi and Xj are data points. 10. The computer implemented system of claim 9, wherein said hyper plane is obtained in accordance with an equation: f  ( X ) = ∑ ( i ∈ suppor   vectors }  α i  y i  K  ( X i , X ) + b , where i ranges from 1 to n, where αi is said weight assigned to each of said plurality of support vectors ‘i’, and b is the bias. 11. One or more non-transitory machine readable information storage mediums comprising one or more instructions, which when executed by one or more hardware processors causes classifying one or more users by performing the step of: obtaining a data set from one or more sources, wherein said data set comprises information corresponding to one or more users; segmenting said data set into one or more segments using a decision tree, each segment comprising one or more data points, wherein each data point is specific and unique to a user, and wherein said decision tree comprises one or more nodes, each node comprising at least one data point; identifying a plurality of difficult regions from said one or more segments to obtain one or more identified difficult regions, wherein each of said one or more identified difficult regions comprises a set of data points; computing a standard deviation of distances between said set of data points within each of said one or more identified difficult regions for each of said one or more identified difficult regions; selecting a radial basis function (RBF) kernel based on said standard deviation computed for each of said one or more identified difficult regions to obtain a selected RBF kernel; generating a support vector machine model on each of said one or more identified difficult regions based on said selected RBF kernel to obtain a hyper plane comprising a plurality of support vectors and weight assigned to each of said plurality of support vectors; and classifying said one or more users into at least one category based on said hyper plan.