In this paper we present a variational Bayes (VB) framework for learning continuous hidden Markov models (CHMMs), and we examine the VB framework within active learning. Unlike a ...
Learning graphical models with hidden variables can offer semantic insights to complex data and lead to salient structured predictors without relying on expensive, sometime unatta...
Hidden Markov models (HMMs) are a powerful probabilistic tool for modeling sequential data, and have been applied with success to many text-related tasks, such as part-of-speech t...
Andrew McCallum, Dayne Freitag, Fernando C. N. Per...
We consider a finite-state memoryless channel with i.i.d. channel state and the input Markov process supported on a mixing finite-type constraint. We discuss the asymptotic behavio...
This paper studies evolutionary clustering, which is a recently hot topic with many important applications, noticeably in social network analysis. In this paper, based on the rece...
Tianbing Xu, Zhongfei (Mark) Zhang, Philip S. Yu, ...