1. A computer implemented method for performing parallel grouping decomposition to facilitate expedited training of a support vector machine (SVM), comprising: receiving a training dataset comprised of data vectors; determining whether any data vector in the dataset violates a set of Karush-Kuhn-Tucker (KKT) conditions associated with a current SVM; dividing the violating data vectors into a number of subsets containing approximately equal proportions of positive and negative examples in each subset, wherein the number of groups can be specified as an adjustable parameter, thereby allowing parallel SVM training for each subset; adding to each subset support vectors associated with the current SVM, wherein the support vectors are those data vectors in the dataset for which there is a corresponding number between zero and a constant, wherein the constant represents an amount of penalty for an incorrect classification and the corresponding number is a Lagrange multiplier corresponding to an inequality constraint in a prime problem; building at least one independent SVM for each subset in parallel; constructing a new SVM to replace the current SVM by forming a set that contains support vectors from each of the independent SVMs; receiving test data comprised of one or more observations of one or more physical characteristics of a physical object under test; and producing a result to indicate a physical state the object under test is in, based on the test data and the SVM.