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    » Covariance Kernels from Bayesian Generative Models
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    ECML
    2006
    Springer
    145views Machine Learning» more  ECML 2006»
    Fisher Kernels for Relational Data
    13 years 11 months ago
    Fisher Kernels for Relational Data
    Download  www-kd.iai.uni-bonn.de
    Abstract. Combining statistical and relational learning receives currently a lot of attention. The majority of statistical relational learning approaches focus on density estimatio...
    Uwe Dick, Kristian Kersting
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    NIPS
    2003
    151views Information Technology» more  NIPS 2003»
    Kernel Dimensionality Reduction for Supervised Learning
    13 years 9 months ago
    Kernel Dimensionality Reduction for Supervised Learning
    Download  books.nips.cc
    We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
    Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
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    NIPS
    2007
    180views Information Technology» more  NIPS 2007»
    Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
    13 years 9 months ago
    Bayesian binning beats approximate alternatives: estimating peri-stimulus time histograms
    Download  research-repository.st-andrews.ac.uk
    The peristimulus time histogram (PSTH) and its more continuous cousin, the spike density function (SDF) are staples in the analytic toolkit of neurophysiologists. The former is us...
    Dominik Endres, Mike W. Oram, Johannes E. Schindel...
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    CSDA
    2010
    165views more  CSDA 2010»
    A two-component Weibull mixture to model early and late mortality in a Bayesian framework
    13 years 7 months ago
    A two-component Weibull mixture to model early and late mortality in a Bayesian framework
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    A two component parametric mixture is proposed to model survival after an invasive treatment, when patients may experience different hazards regimes: a risk of early mortality dir...
    Alessio Farcomeni, Alessandra Nardi
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    NIPS
    2007
    190views Information Technology» more  NIPS 2007»
    Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
    13 years 9 months ago
    Bayesian Inference for Spiking Neuron Models with a Sparsity Prior
    Download  books.nips.cc
    Generalized linear models are the most commonly used tools to describe the stimulus selectivity of sensory neurons. Here we present a Bayesian treatment of such models. Using the ...
    Sebastian Gerwinn, Jakob Macke, Matthias Seeger, M...
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