1. A method, comprising: receiving a set of features extracted from input data; training a linear classifier based on the set of features extracted; generating a first matrix using the linear classifier, wherein the first matrix includes multiple dimensions, wherein each dimension includes multiple elements, wherein elements of a first dimension correspond to the set of features extracted, and wherein elements of a second dimension correspond to a set of classification labels; arranging the elements of the second dimension based on one or more synaptic weight arrangements, wherein each synaptic weight arrangement represents effective synaptic strengths for a classification label of the set of classification labels; and programming a neurosynaptic core circuit with synaptic connectivity information based on the one or more synaptic weight arrangements, wherein the core circuit is configured to classify one or more objects of interest in the input data.