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    ICML
    2005
    IEEE
    105views Machine Learning» more  ICML 2005»
    Fast maximum margin matrix factorization for collaborative prediction
    14 years 8 months ago
    Fast maximum margin matrix factorization for collaborative prediction
    Download  people.csail.mit.edu
    Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard facto...
    Jason D. M. Rennie, Nathan Srebro
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    ICML
    2005
    IEEE
    105views Machine Learning» more  ICML 2005»
    Generalized skewing for functions with continuous and nominal attributes
    14 years 8 months ago
    Generalized skewing for functions with continuous and nominal attributes
    Download  www.machinelearning.org
    This paper extends previous work on skewing, an approach to problematic functions in decision tree induction. The previous algorithms were applicable only to functions of binary v...
    Soumya Ray, David Page
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    ICML
    2005
    IEEE
    112views Machine Learning» more  ICML 2005»
    Expectation maximization algorithms for conditional likelihoods
    14 years 8 months ago
    Expectation maximization algorithms for conditional likelihoods
    Download  www.cis.hut.fi
    We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
    Jarkko Salojärvi, Kai Puolamäki, Samuel ...
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    ICML
    2005
    IEEE
    124views Machine Learning» more  ICML 2005»
    Supervised versus multiple instance learning: an empirical comparison
    14 years 8 months ago
    Supervised versus multiple instance learning: an empirical comparison
    Download  www.biostat.wisc.edu
    We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
    Soumya Ray, Mark Craven
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    ICML
    2005
    IEEE
    144views Machine Learning» more  ICML 2005»
    Healing the relevance vector machine through augmentation
    14 years 8 months ago
    Healing the relevance vector machine through augmentation
    Download  www.machinelearning.org
    The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
    Carl Edward Rasmussen, Joaquin Quiñonero Ca...
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    ICML
    2005
    IEEE
    165views Machine Learning» more  ICML 2005»
    Supervised dimensionality reduction using mixture models
    14 years 8 months ago
    Supervised dimensionality reduction using mixture models
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    Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
    Sajama, Alon Orlitsky
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    ICML
    2005
    IEEE
    123views Machine Learning» more  ICML 2005»
    A model for handling approximate, noisy or incomplete labeling in text classification
    14 years 8 months ago
    A model for handling approximate, noisy or incomplete labeling in text classification
    Download  www.cse.iitb.ac.in
    We introduce a Bayesian model, BayesANIL, that is capable of estimating uncertainties associated with the labeling process. Given a labeled or partially labeled training corpus of...
    Ganesh Ramakrishnan, Krishna Prasad Chitrapura, Ra...
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    ICML
    2005
    IEEE
    126views Machine Learning» more  ICML 2005»
    Learning hierarchical multi-category text classification models
    14 years 8 months ago
    Learning hierarchical multi-category text classification models
    Download  www.machinelearning.org
    We present a kernel-based algorithm for hierarchical text classification where the documents are allowed to belong to more than one category at a time. The classification model is...
    Craig Saunders, John Shawe-Taylor, Juho Rousu, S&a...
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    ICML
    2005
    IEEE
    121views Machine Learning» more  ICML 2005»
    Integer linear programming inference for conditional random fields
    14 years 8 months ago
    Integer linear programming inference for conditional random fields
    Download  l2r.cs.uiuc.edu
    Dan Roth, Wen-tau Yih
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    ICML
    2005
    IEEE
    118views Machine Learning» more  ICML 2005»
    Why skewing works: learning difficult Boolean functions with greedy tree learners
    14 years 8 months ago
    Why skewing works: learning difficult Boolean functions with greedy tree learners
    Download  www.machinelearning.org
    We analyze skewing, an approach that has been empirically observed to enable greedy decision tree learners to learn "difficult" Boolean functions, such as parity, in the...
    Bernard Rosell, Lisa Hellerstein, Soumya Ray, Davi...
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