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    NIPS
    2003
    107views Information Technology» more  NIPS 2003»
    Bias-Corrected Bootstrap and Model Uncertainty
    13 years 8 months ago
    Bias-Corrected Bootstrap and Model Uncertainty
    Download  books.nips.cc
    The bootstrap has become a popular method for exploring model (structure) uncertainty. Our experiments with artificial and realworld data demonstrate that the graphs learned from...
    Harald Steck, Tommi Jaakkola
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    ICML
    2005
    IEEE
    96views Machine Learning» more  ICML 2005»
    Closed-form dual perturb and combine for tree-based models
    14 years 8 months ago
    Closed-form dual perturb and combine for tree-based models
    Download  www.montefiore.ulg.ac.be
    This paper studies the aggregation of predictions made by tree-based models for several perturbed versions of the attribute vector of a test case. A closed-form approximation of t...
    Pierre Geurts, Louis Wehenkel
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    ICML
    2005
    IEEE
    145views Machine Learning» more  ICML 2005»
    Bayesian hierarchical clustering
    14 years 8 months ago
    Bayesian hierarchical clustering
    Download  www.gatsby.ucl.ac.uk
    We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages ove...
    Katherine A. Heller, Zoubin Ghahramani
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    NIPS
    1997
    96views Information Technology» more  NIPS 1997»
    Learning Generative Models with the Up-Propagation Algorithm
    13 years 8 months ago
    Learning Generative Models with the Up-Propagation Algorithm
    Download  hebb.mit.edu
    Up-propagation is an algorithm for inverting and learning neural network generative models. Sensory input is processed by inverting a model that generates patterns from hidden var...
    Jong-Hoon Oh, H. Sebastian Seung
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    JMLR
    2002
    115views more  JMLR 2002»
    PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
    13 years 7 months ago
    PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
    Download  jmlr.csail.mit.edu
    Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
    Matthias Seeger
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