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    COLT
    2007
    Springer
    128views Machine Learning» more  COLT 2007»
    Rademacher Margin Complexity
    14 years 5 months ago
    Rademacher Margin Complexity
    Download  www.cis.pku.edu.cn
    Liwei Wang, Jufu Feng
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    ESANN
    2006
    85views Neural Networks» more  ESANN 2006»
    Synthesis of maximum margin and multiview learning using unlabeled data
    14 years 4 days ago
    Synthesis of maximum margin and multiview learning using unlabeled data
    Download  www.dice.ucl.ac.be
    In this presentation we show the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary SVM. Our formulation exp...
    Sándor Szedmák, John Shawe-Taylor
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    NIPS
    2008
    89views Information Technology» more  NIPS 2008»
    Rademacher Complexity Bounds for Non-I.I.D. Processes
    14 years 5 days ago
    Rademacher Complexity Bounds for Non-I.I.D. Processes
    Download  www.cs.nyu.edu
    This paper presents the first Rademacher complexity-based error bounds for noni.i.d. settings, a generalization of similar existing bounds derived for the i.i.d. case. Our bounds ...
    Mehryar Mohri, Afshin Rostamizadeh
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    NIPS
    2008
    124views Information Technology» more  NIPS 2008»
    On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
    14 years 5 days ago
    On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
    Download  ttic.uchicago.edu
    This work characterizes the generalization ability of algorithms whose predictions are linear in the input vector. To this end, we provide sharp bounds for Rademacher and Gaussian...
    Sham M. Kakade, Karthik Sridharan, Ambuj Tewari
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    JMLR
    2010
    121views more  JMLR 2010»
    Sparse Semi-supervised Learning Using Conjugate Functions
    13 years 5 months ago
    Sparse Semi-supervised Learning Using Conjugate Functions
    Download  jmlr.csail.mit.edu
    In this paper, we propose a general framework for sparse semi-supervised learning, which concerns using a small portion of unlabeled data and a few labeled data to represent targe...
    Shiliang Sun, John Shawe-Taylor
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