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    ICANN
    2001
    Springer
    134views Neural Networks» more  ICANN 2001»
    On-Line Learning Methods for Gaussian Processes
    14 years 1 months ago
    On-Line Learning Methods for Gaussian Processes
    Download  www.jst.go.jp
    Shigeyuki Oba, Masa-aki Sato, Shin Ishii
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    INFOCOM
    2006
    IEEE
    104views Communications» more  INFOCOM 2006»
    An Integrated Framework for Managing Best Effort Traffic in MPLS Networks
    14 years 2 months ago
    An Integrated Framework for Managing Best Effort Traffic in MPLS Networks
    Download  net.cs.uni-tuebingen.de
    — This paper presents a scalable and flexible system for explicit rate congestion control in Multi-Protocol Label Switched networks. Resource Reservation Protocol is used to sign...
    Christopher Marty, Mohamed A. Ali
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    ML
    2006
    ACM
    142views Machine Learning» more  ML 2006»
    The max-min hill-climbing Bayesian network structure learning algorithm
    13 years 8 months ago
    The max-min hill-climbing Bayesian network structure learning algorithm
    Download  www.dsl-lab.org
    We present a new algorithm for Bayesian network structure learning, called Max-Min Hill-Climbing (MMHC). The algorithm combines ideas from local learning, constraint-based, and sea...
    Ioannis Tsamardinos, Laura E. Brown, Constantin F....
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    ICDAR
    2009
    IEEE
    170views Document Analysis» more  ICDAR 2009»
    Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation
    14 years 3 months ago
    Statistical Modeling and Learning for Recognition-Based Handwritten Numeral String Segmentation
    Download  nlpr-web.ia.ac.cn
    This paper proposes a recognition based approach to handwritten numeral string segmentation. We consider two classes: numeral strings segmented correctly or not. The feature vecto...
    Yanjie Wang, Xiabi Liu, Yunde Jia
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    NN
    2010
    Springer
    187views Neural Networks» more  NN 2010»
    Efficient exploration through active learning for value function approximation in reinforcement learning
    13 years 3 months ago
    Efficient exploration through active learning for value function approximation in reinforcement learning
    Download  sugiyama-www.cs.titech.ac.jp
    Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
    Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
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