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    ICML
    2006
    IEEE
    144views Machine Learning» more  ICML 2006»
    Probabilistic inference for solving discrete and continuous state Markov Decision Processes
    14 years 8 months ago
    Probabilistic inference for solving discrete and continuous state Markov Decision Processes
    Download  eprints.pascal-network.org
    Inference in Markov Decision Processes has recently received interest as a means to infer goals of an observed action, policy recognition, and also as a tool to compute policies. ...
    Marc Toussaint, Amos J. Storkey
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    ICML
    2008
    IEEE
    144views Machine Learning» more  ICML 2008»
    An HDP-HMM for systems with state persistence
    14 years 8 months ago
    An HDP-HMM for systems with state persistence
    Download  www.cs.berkeley.edu
    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,...
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    ICML
    2003
    IEEE
    133views Machine Learning» more  ICML 2003»
    Hierarchical Latent Knowledge Analysis for Co-occurrence Data
    14 years 8 months ago
    Hierarchical Latent Knowledge Analysis for Co-occurrence Data
    Download  www.aaai.org
    Hiroshi Mamitsuka
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    CSREASAM
    2004
    138views Security Privacy» more  CSREASAM 2004»
    The Key Authority - Secure Key Management in Hierarchical Public Key Infrastructures
    13 years 8 months ago
    The Key Authority - Secure Key Management in Hierarchical Public Key Infrastructures
    Download  www.cdc.informatik.tu-darmstadt.de
    We model a private key's life cycle as a finite state machine. The states are the key's phases of life and the transition functions describe tasks to be done with the key...
    Alexander Wiesmaier, Marcus Lippert, Vangelis Kara...
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    NIPS
    1997
    94views Information Technology» more  NIPS 1997»
    Reinforcement Learning with Hierarchies of Machines
    13 years 8 months ago
    Reinforcement Learning with Hierarchies of Machines
    Download  www.cs.berkeley.edu
    We present a new approach to reinforcement learning in which the policies considered by the learning process are constrained by hierarchies of partially specified machines. This ...
    Ronald Parr, Stuart J. Russell
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