Online learning algorithms have recently risen to prominence due to their strong theoretical guarantees and an increasing number of practical applications for large-scale data ana...
Moving point object data can be analyzed through the discovery of patterns. We consider the computational efficiency of computing two of the most basic spatio-temporal patterns in...
Semidefinite programs (SDP) have been used in many recent approximation algorithms. We develop a general primal-dual approach to solve SDPs using a generalization of the well-know...
We present an architecture and an on-line learning algorithm and apply it to the problem of part-ofspeech tagging. The architecture presented, SNOW, is a network of linear separat...
Designing efficient sorting networks has been a challenging combinatorial optimization problem since the early 1960’s. The application of evolutionary computing to this problem ...