kernelLogisticRegression

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.

License	GNU General Public License 3
Categories	Weka Business Logic Libraries Machine Learning
GroupId	nz.ac.waikato.cms.weka
ArtifactId	kernelLogisticRegression
Last Version	1.0.0
Release Date	Jun 26, 2013
Type	jar
Description	kernelLogisticRegression 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.
Project URL	http://weka.sourceforge.net/doc.packages/kernelLogisticRegression
Project Organization	University of Waikato, Hamilton, NZ
Source Code Management	https://svn.cms.waikato.ac.nz/svn/weka/tags/kernelLogisticRegression-1.0.0

Download kernelLogisticRegression

Filename	Size
kernelLogisticRegression-1.0.0.pom
kernelLogisticRegression-1.0.0.jar	16 KB
kernelLogisticRegression-1.0.0-tests.jar	4 KB
kernelLogisticRegression-1.0.0-test-sources.jar	1 KB
kernelLogisticRegression-1.0.0-sources.jar	7 KB
kernelLogisticRegression-1.0.0-javadoc.jar	44 KB

How to add to project

Apache Maven

<!-- https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/kernelLogisticRegression/ -->
<dependency>
    <groupId>nz.ac.waikato.cms.weka</groupId>
    <artifactId>kernelLogisticRegression</artifactId>
    <version>1.0.0</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/kernelLogisticRegression/
implementation 'nz.ac.waikato.cms.weka:kernelLogisticRegression:1.0.0'

Gradle Kotlin

// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/kernelLogisticRegression/
implementation ("nz.ac.waikato.cms.weka:kernelLogisticRegression:1.0.0")

Apache Buildr

'nz.ac.waikato.cms.weka:kernelLogisticRegression:jar:1.0.0'

Apache Ivy

<dependency org="nz.ac.waikato.cms.weka" name="kernelLogisticRegression" rev="1.0.0">
  <artifact name="kernelLogisticRegression" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='nz.ac.waikato.cms.weka', module='kernelLogisticRegression', version='1.0.0')
)

Scala SBT

libraryDependencies += "nz.ac.waikato.cms.weka" % "kernelLogisticRegression" % "1.0.0"

Leiningen

[nz.ac.waikato.cms.weka/kernelLogisticRegression "1.0.0"]

Dependencies

compile (1)

Group / Artifact	Type	Version
nz.ac.waikato.cms.weka : weka-dev	jar	[3.7.6,)

test (2)

Group / Artifact	Type	Version
nz.ac.waikato.cms.weka : weka-dev	test-jar	[3.7.6,)
junit : junit	jar	3.8.2

Project Modules

There are no modules declared in this project.

Versions

Version
1.0.0 Jun 26, 2013