We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
We present a hierarchical generative model for object recognition that is constructed by weakly-supervised learning. A key component is a novel, adaptive patch feature whose width...
We examine the ability to exploit the hierarchical structure of Internet addresses in order to endow network agents with predictive capabilities. Specifically, we consider Suppor...
We consider the least-square regression problem with regularization by a block 1-norm, that is, a sum of Euclidean norms over spaces of dimensions larger than one. This problem, r...
Accurate and timely traffic classification is critical in network security monitoring and traffic engineering. Traditional methods based on port numbers and protocols have proven t...