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ICML
2010
IEEE
15 years 8 months ago
Hilbert Space Embeddings of Hidden Markov Models
Hidden Markov Models (HMMs) are important tools for modeling sequence data. However, they are restricted to discrete latent states, and are largely restricted to Gaussian and disc...
Le Song, Sajid M. Siddiqi, Geoffrey J. Gordon, Ale...
IPL
2008
131views more  IPL 2008»
15 years 7 months ago
Ant colony optimization with partial order reduction for discovering safety property violations in concurrent models
In this article we analyze the combination of ACOhg, a new metaheuristic algorithm, plus partial order reduction applied to the problem of finding safety property violations in co...
J. Francisco Chicano, Enrique Alba
KI
2008
Springer
15 years 7 months ago
Interpreting Motion Expressions in Route Instructions Using Two Projection-Based Spatial Models
This paper explores the applicability of two formal models of spatial relations, Double Cross and RfDL3-12, to interpret some typical expressions that people use for describing a r...
Yohei Kurata, Hui Shi
FTSIG
2007
136views more  FTSIG 2007»
15 years 7 months ago
The Application of Hidden Markov Models in Speech Recognition
Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a consequence, almost all present day large vocabula...
Mark J. F. Gales, Steve Young
TSP
2008
103views more  TSP 2008»
15 years 7 months ago
Low-Rank Variance Approximation in GMRF Models: Single and Multiscale Approaches
Abstract--We present a versatile framework for tractable computation of approximate variances in large-scale Gaussian Markov random field estimation problems. In addition to its ef...
Dmitry M. Malioutov, Jason K. Johnson, Myung Jin C...