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kernelLogisticRegression from group nz.ac.waikato.cms.weka (version 1.0.0)
This package contains a classifier that can be used to train a two-class kernel logistic regression model with the kernel functions that are available in WEKA. It optimises the negative log-likelihood with a quadratic penalty. Both, BFGS and conjugate gradient descent, are available as optimisation methods, but the former is normally faster. It is possible to use multiple threads, but the speed-up is generally very marginal when used with BFGS optimisation. With conjugate gradient descent optimisation, greater speed-ups can be achieved when using multiple threads. With the default kernel, the dot product kernel, this method produces results that are close to identical to those obtained using standard logistic regression in WEKA, provided a sufficiently large value for the parameter determining the size of the quadratic penalty is used in both cases.
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Artifact kernelLogisticRegression
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 26. June 2013
Tags: methods using greater both package regression contains used close results threads quadratic when value train penalty weka class marginal available conjugate size optimises that default determining identical this large optimisation obtained model former bfgs likelihood normally faster possible achieved parameter sufficiently generally multiple functions those with cases standard speed descent logistic kernel product produces classifier provided method negative very gradient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/kernelLogisticRegression
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
Group nz.ac.waikato.cms.weka
Version 1.0.0
Last update 26. June 2013
Tags: methods using greater both package regression contains used close results threads quadratic when value train penalty weka class marginal available conjugate size optimises that default determining identical this large optimisation obtained model former bfgs likelihood normally faster possible achieved parameter sufficiently generally multiple functions those with cases standard speed descent logistic kernel product produces classifier provided method negative very gradient
Organization University of Waikato, Hamilton, NZ
URL http://weka.sourceforge.net/doc.packages/kernelLogisticRegression
License GNU General Public License 3
Dependencies amount 1
Dependencies weka-dev,
There are maybe transitive dependencies!
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