1. A method for analyzing input data-space by learning one or more classifiers, the method comprising the steps of: choosing a random candidate subset from a predetermined training data-set that is used to analyze the input data-space, wherein the candidate subset comprising candidates; adding one or more candidates temporarily from the candidate subset to an expansion set to generate a new kernel space for the input data-space by predetermined repeated evaluations of leave-one-out errors for the candidates added to the expansion set; removing the candidates temporarily added to the expansion set after the leave-one-out error evaluations are performed; selecting the candidates to be permanently added to the expansion set based on the leave-one-out errors of the candidates temporarily added to the expansion set; and determining the one or more classifiers using the candidates permanently added to the expansion set.