1. A method for enabling machine learning, comprising: receiving a support vector comprising at least one feature represented by one non-zero entry; identifying at least one column within a matrix with non-zero entries, wherein said at least one column is identified in accordance with said at least one feature of said support vector; and performing, by a processor, kernel computations using successive list merging on said at least one identified column of said matrix and said support vector to derive a result vector, wherein said result vector is used in a data learning function, wherein said data learning function is applied to a machine learning application, wherein said performing comprises: performing an iteration of merging of a first of said at least one identified column as an initial result vector and a next identified column vector to produce an updated result vector; using said updated result vector as the initial result vector for a next iteration of merging until all identified columns have been processed; and wherein said merging comprises performing multiplication and addition computations between an entry of said initial result vector and an entry of said next identified column vector if said entry of said initial result vector comprises an index matching an index of said entry of said next identified column vector to produce a corresponding entry in the updated result vector.