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    CN
    2010
    110views more  CN 2010»
    End-to-end quality of service seen by applications: A statistical learning approach
    15 years 6 months ago
    End-to-end quality of service seen by applications: A statistical learning approach
    Download  iie.fing.edu.uy
    The focus of this work is on the estimation of quality of service (QoS) parameters seen by an application. Our proposal is based on end-to-end active measurements and statistical ...
    Pablo Belzarena, Laura Aspirot
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    JMLR
    2012
    196views Programming Languages» more  JMLR 2012»
    A metric learning perspective of SVM: on the relation of LMNN and SVM
    13 years 9 months ago
    A metric learning perspective of SVM: on the relation of LMNN and SVM
    Download  jmlr.csail.mit.edu
    Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper...
    Huyen Do, Alexandros Kalousis, Jun Wang, Adam Wozn...
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    ICML
    2009
    IEEE
    140views Machine Learning» more  ICML 2009»
    Learning dictionaries of stable autoregressive models for audio scene analysis
    16 years 7 months ago
    Learning dictionaries of stable autoregressive models for audio scene analysis
    Download  cseweb.ucsd.edu
    In this paper, we explore an application of basis pursuit to audio scene analysis. The goal of our work is to detect when certain sounds are present in a mixed audio signal. We fo...
    Youngmin Cho, Lawrence K. Saul
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    ICML
    2009
    IEEE
    309views Machine Learning» more  ICML 2009»
    Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    16 years 7 months ago
    Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
    Download  www.cnbc.cmu.edu
    There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images re...
    Honglak Lee, Roger Grosse, Rajesh Ranganath, Andre...
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    ICML
    2009
    IEEE
    134views Machine Learning» more  ICML 2009»
    Learning Markov logic network structure via hypergraph lifting
    16 years 7 months ago
    Learning Markov logic network structure via hypergraph lifting
    Download  www.cs.washington.edu
    Markov logic networks (MLNs) combine logic and probability by attaching weights to first-order clauses, and viewing these as templates for features of Markov networks. Learning ML...
    Stanley Kok, Pedro Domingos
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