1. A method for learning a binary classifier for classifying samples into a first class and a second class, comprising: acquiring a set of training samples, each training sample labeled as either belonging to the first class or to the second class; connecting pairs of dyadic samples by projection vectors, a first sample of each dyadic pair belonging to the first class and a second sample of each dyadic pair belonging to the second class; forming a set of hyperplanes having a surface normal to the projection vectors; selecting one hyperplane from the set of hyperplanes, the selected hyperplane minimizing a weighted classification error; weighting the set of training samples according to a classification by the selected hyperplane; combining the selected hyperplanes into a binary classifier; and repeating the selecting, weighting, and combining a predetermined number of iterations to obtain a final classifier for classifying test samples into the first and second classes.