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NIPS
1998
13 years 9 months ago
Risk Sensitive Reinforcement Learning
In this paper, we consider Markov Decision Processes (MDPs) with error states. Error states are those states entering which is undesirable or dangerous. We define the risk with re...
Ralph Neuneier, Oliver Mihatsch
NIPS
1996
13 years 9 months ago
Multidimensional Triangulation and Interpolation for Reinforcement Learning
Dynamic Programming, Q-learning and other discrete Markov Decision Process solvers can be applied to continuous d-dimensional state-spaces by quantizing the state space into an arr...
Scott Davies
BMCBI
2010
159views more  BMCBI 2010»
13 years 7 months ago
Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines
Background: Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods ha...
Alvaro J. González, Li Liao
BMCBI
2008
132views more  BMCBI 2008»
13 years 7 months ago
Clustering ionic flow blockade toggles with a Mixture of HMMs
Background: Ionic current blockade signal processing, for use in nanopore detection, offers a promising new way to analyze single molecule properties with potential implications f...
Alexander G. Churbanov, Stephen Winters-Hilt
CSL
2010
Springer
13 years 7 months ago
Bayesian update of dialogue state: A POMDP framework for spoken dialogue systems
This paper describes a statistically motivated framework for performing real-time dialogue state updates and policy learning in a spoken dialogue system. The framework is based on...
Blaise Thomson, Steve Young
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