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    JMLR
    2008
    169views more  JMLR 2008»
    Multi-class Discriminant Kernel Learning via Convex Programming
    13 years 9 months ago
    Multi-class Discriminant Kernel Learning via Convex Programming
    Download  jmlr.csail.mit.edu
    Regularized kernel discriminant analysis (RKDA) performs linear discriminant analysis in the feature space via the kernel trick. Its performance depends on the selection of kernel...
    Jieping Ye, Shuiwang Ji, Jianhui Chen
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    JMLR
    2008
    73views more  JMLR 2008»
    On the Suitable Domain for SVM Training in Image Coding
    13 years 9 months ago
    On the Suitable Domain for SVM Training in Image Coding
    Download  www.uv.es
    Conventional SVM-based image coding methods are founded on independently restricting the distortion in every image coefficient at some particular image representation. Geometrical...
    Gustavo Camps-Valls, Juan Gutierrez, Gabriel G&oac...
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    JMLR
    2008
    159views more  JMLR 2008»
    Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
    13 years 9 months ago
    Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies
    Download  www.cs.cmu.edu
    When monitoring spatial phenomena, which can often be modeled as Gaussian processes (GPs), choosing sensor locations is a fundamental task. There are several common strategies to ...
    Andreas Krause, Ajit Paul Singh, Carlos Guestrin
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    CSDA
    2007
    264views more  CSDA 2007»
    Model-based methods to identify multiple cluster structures in a data set
    13 years 9 months ago
    Model-based methods to identify multiple cluster structures in a data set
    Download  www.ba.unibo.it
    Model-based clustering exploits finite mixture models for detecting group in a data set. It provides a sound statistical framework which can address some important issues, such as...
    Giuliano Galimberti, Gabriele Soffritti
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    JACM
    2006
    99views more  JACM 2006»
    Finding a maximum likelihood tree is hard
    13 years 9 months ago
    Finding a maximum likelihood tree is hard
    Download  www.cs.tau.ac.il
    Abstract. Maximum likelihood (ML) is an increasingly popular optimality criterion for selecting evolutionary trees [Felsenstein 1981]. Finding optimal ML trees appears to be a very...
    Benny Chor, Tamir Tuller
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