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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.

Лицензия

Лицензия
Категории

Категории

Weka Прикладные библиотеки Machine Learning
Группа

Группа

nz.ac.waikato.cms.weka
Идентификатор

Идентификатор

kernelLogisticRegression
Последняя версия

Последняя версия

1.0.0
Дата

Дата
Тип

Тип

jar
Описание

Описание

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.
Ссылка на сайт

Ссылка на сайт

http://weka.sourceforge.net/doc.packages/kernelLogisticRegression
Организация-разработчик

Организация-разработчик

University of Waikato, Hamilton, NZ
Система контроля версий

Система контроля версий

https://svn.cms.waikato.ac.nz/svn/weka/tags/kernelLogisticRegression-1.0.0

Скачать kernelLogisticRegression

Имя Файла Размер
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
Обзор

Как подключить последнюю версию

<!-- 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>
// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/kernelLogisticRegression/
implementation 'nz.ac.waikato.cms.weka:kernelLogisticRegression:1.0.0'
// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/kernelLogisticRegression/
implementation ("nz.ac.waikato.cms.weka:kernelLogisticRegression:1.0.0")
'nz.ac.waikato.cms.weka:kernelLogisticRegression:jar:1.0.0'
<dependency org="nz.ac.waikato.cms.weka" name="kernelLogisticRegression" rev="1.0.0">
  <artifact name="kernelLogisticRegression" type="jar" />
</dependency>
@Grapes(
@Grab(group='nz.ac.waikato.cms.weka', module='kernelLogisticRegression', version='1.0.0')
)
libraryDependencies += "nz.ac.waikato.cms.weka" % "kernelLogisticRegression" % "1.0.0"
[nz.ac.waikato.cms.weka/kernelLogisticRegression "1.0.0"]

Зависимости

compile (1)

Идентификатор библиотеки Тип Версия
nz.ac.waikato.cms.weka : weka-dev jar [3.7.6,)

test (2)

Идентификатор библиотеки Тип Версия
nz.ac.waikato.cms.weka : weka-dev test-jar [3.7.6,)
junit : junit jar 3.8.2

Модули Проекта

Данный проект не имеет модулей.

Версии библиотеки

Версия
1.0.0