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    NIPS
    2008
    215views Information Technology» more  NIPS 2008»
    Learning Hybrid Models for Image Annotation with Partially Labeled Data
    14 years 29 days ago
    Learning Hybrid Models for Image Annotation with Partially Labeled Data
    Download  www.cs.toronto.edu
    Extensive labeled data for image annotation systems, which learn to assign class labels to image regions, is difficult to obtain. We explore a hybrid model framework for utilizing...
    Xuming He, Richard S. Zemel
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    NIPS
    2008
    205views Information Technology» more  NIPS 2008»
    Dimensionality Reduction for Data in Multiple Feature Representations
    14 years 29 days ago
    Dimensionality Reduction for Data in Multiple Feature Representations
    Download  www.csie.ntu.edu.tw
    In solving complex visual learning tasks, adopting multiple descriptors to more precisely characterize the data has been a feasible way for improving performance. These representa...
    Yen-Yu Lin, Tyng-Luh Liu, Chiou-Shann Fuh
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    NIPS
    2008
    167views Information Technology» more  NIPS 2008»
    Characteristic Kernels on Groups and Semigroups
    14 years 29 days ago
    Characteristic Kernels on Groups and Semigroups
    Download  www.ism.ac.jp
    Embeddings of random variables in reproducing kernel Hilbert spaces (RKHSs) may be used to conduct statistical inference based on higher order moments. For sufficiently rich (char...
    Kenji Fukumizu, Bharath K. Sriperumbudur, Arthur G...
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    NIPS
    2008
    230views Information Technology» more  NIPS 2008»
    Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform
    14 years 29 days ago
    Covariance Estimation for High Dimensional Data Vectors Using the Sparse Matrix Transform
    Download  engineering.purdue.edu
    Covariance estimation for high dimensional vectors is a classically difficult problem in statistical analysis and machine learning. In this paper, we propose a maximum likelihood ...
    Guangzhi Cao, Charles A. Bouman
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    NIPS
    2008
    150views Information Technology» more  NIPS 2008»
    Fast Rates for Regularized Objectives
    14 years 29 days ago
    Fast Rates for Regularized Objectives
    Download  ttic.uchicago.edu
    We study convergence properties of empirical minimization of a stochastic strongly convex objective, where the stochastic component is linear. We show that the value attained by t...
    Karthik Sridharan, Shai Shalev-Shwartz, Nathan Sre...
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    NIPS
    2008
    218views Information Technology» more  NIPS 2008»
    Bayesian Exponential Family PCA
    14 years 29 days ago
    Bayesian Exponential Family PCA
    Download  www.gatsby.ucl.ac.uk
    Principal Components Analysis (PCA) has become established as one of the key tools for dimensionality reduction when dealing with real valued data. Approaches such as exponential ...
    Shakir Mohamed, Katherine A. Heller, Zoubin Ghahra...
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    NIPS
    2008
    183views Information Technology» more  NIPS 2008»
    ICA based on a Smooth Estimation of the Differential Entropy
    14 years 29 days ago
    ICA based on a Smooth Estimation of the Differential Entropy
    Download  eprints.pascal-network.org
    In this paper we introduce the MeanNN approach for estimation of main information theoretic measures such as differential entropy, mutual information and divergence. As opposed to...
    Lev Faivishevsky, Jacob Goldberger
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    NIPS
    2008
    143views Information Technology» more  NIPS 2008»
    An Online Algorithm for Maximizing Submodular Functions
    14 years 29 days ago
    An Online Algorithm for Maximizing Submodular Functions
    Download  www.cs.cmu.edu
    We present an algorithm for solving a broad class of online resource allocation . Our online algorithm can be applied in environments where abstract jobs arrive one at a time, and...
    Matthew J. Streeter, Daniel Golovin
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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 29 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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    NIPS
    2008
    215views Information Technology» more  NIPS 2008»
    Unlabeled data: Now it helps, now it doesn't
    14 years 29 days ago
    Unlabeled data: Now it helps, now it doesn't
    Download  www.cs.cmu.edu
    Empirical evidence shows that in favorable situations semi-supervised learning (SSL) algorithms can capitalize on the abundance of unlabeled training data to improve the performan...
    Aarti Singh, Robert D. Nowak, Xiaojin Zhu
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