The Hidden Markov Model (HMM) has been widely used in many applications such as speech recognition. A common challenge for applying the classical HMM is to determine the structure...
We show that it is possible to extend hidden Markov models to have a countably infinite number of hidden states. By using the theory of Dirichlet processes we can implicitly integ...
Matthew J. Beal, Zoubin Ghahramani, Carl Edward Ra...
In this paper we formalize a general model of cryptanalytic time/memory tradeoffs for the inversion of a random function f : {0, 1, . . . , N - 1} {0, 1, . . . , N - 1}. The model...
Monitoring the variables of real world dynamic systems is a difficult task due to their inherent complexity and uncertainty. Particle Filters (PF) perform that task, yielding prob...
— Network reconstruction, i.e., obtaining network structure from data, is a central theme in systems biology, economics, and engineering. Previous work introduced dynamical struc...
Ye Yuan, Guy-Bart Vincent Stan, Sean Warnick, Jorg...
—In host-based intrusion detection systems (HIDS), anomaly detection involves monitoring for significant deviations from normal system behavior. Hidden Markov Models (HMMs) have...
Wael Khreich, Eric Granger, Robert Sabourin, Ali M...
In this work we present a model that uses a Dirichlet Process (DP) with a dynamic spatial constraints to approximate a non-homogeneous hidden Markov model (NHMM). The coefficient ...
Haijun Ren, Leon N. Cooper, Liang Wu, Predrag Nesk...
In Proc. of IEEE Conf. on CVPR'03, Madison, Wisconsin, 2003 We propose a generative model approach to contour tracking against non-stationary clutter and to coping with occlu...