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    AAAI
    2012
    202views Intelligent Agents» more  AAAI 2012»
    A Testbed for Learning by Demonstration from Natural Language and RGB-Depth Video
    13 years 9 months ago
    A Testbed for Learning by Demonstration from Natural Language and RGB-Depth Video
    Download  www.cs.rochester.edu
    We are developing a testbed for learning by demonstration combining spoken language and sensor data in a natural real-world environment. Microsoft Kinect RGBDepth cameras allow us...
    Young Chol Song, Henry A. Kautz
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    PKDD
    1999
    Springer
    140views Data Mining» more  PKDD 1999»
    Learning of Simple Conceptual Graphs from Positive and Negative Examples
    15 years 11 months ago
    Learning of Simple Conceptual Graphs from Positive and Negative Examples
    Download  vorona.hse.ru
    Sergei O. Kuznetsov
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    KDD
    1994
    ACM
    121views Data Mining» more  KDD 1994»
    Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning
    15 years 10 months ago
    Efficiently Constructing Relational Features from Background Knowledge for Inductive Machine Learning
    Download  www.aaai.org
    John M. Aronis, Foster J. Provost
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    LOCA
    2009
    Springer
    196views Information Technology» more  LOCA 2009»
    Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
    15 years 11 months ago
    Activity Recognition from Sparsely Labeled Data Using Multi-Instance Learning
    Download  tahiti.mis.informatik.tu-darmstadt.de
    Abstract. Activity recognition has attracted increasing attention in recent years due to its potential to enable a number of compelling contextaware applications. As most approache...
    Maja Stikic, Bernt Schiele
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    ICANN
    2009
    Springer
    121views Neural Networks» more  ICANN 2009»
    Learning SVMs from Sloppily Labeled Data
    15 years 11 months ago
    Learning SVMs from Sloppily Labeled Data
    Download  www.lif.univ-mrs.fr
    This paper proposes a modelling of Support Vector Machine (SVM) learning to address the problem of learning with sloppy labels. In binary classification, learning with sloppy labe...
    Guillaume Stempfel, Liva Ralaivola
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