Abstract Partially observable Markov decision processes (POMDPs) are a principled mathematical framework for planning under uncertainty, a crucial capability for reliable operation...
Hanna Kurniawati, Yanzhu Du, David Hsu, Wee Sun Le...
Intuitively, any `bag of words' approach in IR should benefit from taking term dependencies into account. Unfortunately, for years the results of exploiting such dependencies ...
Eduard Hoenkamp, Peter Bruza, Dawei Song, Qiang Hu...
We explore the possibility of using both face and gait in enhancing human recognition at a distance performance in outdoor conditions. Although the individual performance of gait ...
Monitoring real-time concurrent systems is a challenging task. In this paper we formulate (model-based) supervision by means of hidden state history reconstruction, from event (e.g...
This paper presents a framework for efficient HMM-based estimation of unreliable spectrographic speech data. It discusses the role of Hidden Markov Models (HMMs) during minimum mea...