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    ML
    2002
    ACM
    220views Machine Learning» more  ML 2002»
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
    13 years 7 months ago
    Bayesian Methods for Support Vector Machines: Evidence and Predictive Class Probabilities
    Download  www.mth.kcl.ac.uk
    I describe a framework for interpreting Support Vector Machines (SVMs) as maximum a posteriori (MAP) solutions to inference problems with Gaussian Process priors. This probabilisti...
    Peter Sollich
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    BMCBI
    2006
    143views more  BMCBI 2006»
    Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms
    13 years 7 months ago
    Discovering functional gene expression patterns in the metabolic network of Escherichia coli with wavelets transforms
    Download  www.biomedcentral.com
    Background: Microarray technology produces gene expression data on a genomic scale for an endless variety of organisms and conditions. However, this vast amount of information nee...
    Rainer König, Gunnar Schramm, Marcus Oswald, ...
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    JMLR
    2008
    168views more  JMLR 2008»
    Max-margin Classification of Data with Absent Features
    13 years 7 months ago
    Max-margin Classification of Data with Absent Features
    Download  ai.stanford.edu
    We consider the problem of learning classifiers in structured domains, where some objects have a subset of features that are inherently absent due to complex relationships between...
    Gal Chechik, Geremy Heitz, Gal Elidan, Pieter Abbe...
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    AUTOMATICA
    2006
    150views more  AUTOMATICA 2006»
    Enlarging the terminal region of nonlinear model predictive control using the support vector machine method
    13 years 7 months ago
    Enlarging the terminal region of nonlinear model predictive control using the support vector machine method
    Download  guppy.mpe.nus.edu.sg
    In this paper, Receding Horizon Model Predictive Control (RHMPC) of nonlinear systems subject to input and state constraints is considered. We propose to estimate the terminal reg...
    Chong Jin Ong, Dan Sui, Elmer G. Gilbert
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    BMCBI
    2007
    139views more  BMCBI 2007»
    Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
    13 years 8 months ago
    Improving model predictions for RNA interference activities that use support vector machine regression by combining and filterin
    Download  www.biomedcentral.com
    Background: RNA interference (RNAi) is a naturally occurring phenomenon that results in the suppression of a target RNA sequence utilizing a variety of possible methods and pathwa...
    Andrew S. Peek
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