We propose a new unsupervised learning technique for extracting information about authors and topics from large text collections. We model documents as if they were generated by a...
Michal Rosen-Zvi, Chaitanya Chemudugunta, Thomas L...
—In this paper we develop an adaptive learning algorithm which is approximately optimal for an opportunistic spectrum access (OSA) problem with polynomial complexity. In this OSA...
We first formulate multiple targets tracking problem in a dynamic Markov network(DMN)which is derived from a MRFs for joint target state and a binary process for occlusion of dual...
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...
Background: Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties, with potential implications ...
Alexander G. Churbanov, Carl Baribault, Stephen Wi...