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ICML
2008
IEEE
15 years 11 months ago
An HDP-HMM for systems with state persistence
The hierarchical Dirichlet process hidden Markov model (HDP-HMM) is a flexible, nonparametric model which allows state spaces of unknown size to be learned from data. We demonstra...
Emily B. Fox, Erik B. Sudderth, Michael I. Jordan,...
ECML
2006
Springer
15 years 1 months ago
PAC-Learning of Markov Models with Hidden State
The standard approach for learning Markov Models with Hidden State uses the Expectation-Maximization framework. While this approach had a significant impact on several practical ap...
Ricard Gavaldà, Philipp W. Keller, Joelle P...
CSCLP
2006
Springer
15 years 1 months ago
A Constraint Model for State Transitions in Disjunctive Resources
Abstract. Traditional resources in scheduling are simple machines where a capacity is the main restriction. However, in practice there frequently appear resources with more complex...
Roman Barták, Ondrej Cepek
ASWEC
2005
IEEE
15 years 3 months ago
A UML Approach to the Generation of Test Sequences for Java-Based Concurrent Systems
Starting with a UML specification that captures the underlying functionality of some given Java-based concurrent system, we describe a systematic way to construct, from this speci...
Soon-Kyeong Kim, Luke Wildman, Roger Duke
NEUROSCIENCE
2001
Springer
15 years 2 months ago
Finite-State Computation in Analog Neural Networks: Steps towards Biologically Plausible Models?
Abstract. Finite-state machines are the most pervasive models of computation, not only in theoretical computer science, but also in all of its applications to real-life problems, a...
Mikel L. Forcada, Rafael C. Carrasco