multiLayerPerceptrons 1.0.10

This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.

License	GNU General Public License 3
Categories	Weka Business Logic Libraries Machine Learning
GroupId	nz.ac.waikato.cms.weka
ArtifactId	multiLayerPerceptrons
Version	1.0.10
Type	jar
Description	multiLayerPerceptrons This package currently contains classes for training multilayer perceptrons with one hidden layer, where the number of hidden units is user specified. MLPClassifier can be used for classification problems and MLPRegressor is the corresponding class for numeric prediction tasks. The former has as many output units as there are classes, the latter only one output unit. Both minimise a penalised squared error with a quadratic penalty on the (non-bias) weights, i.e., they implement "weight decay", where this penalised error is averaged over all training instances. The size of the penalty can be determined by the user by modifying the "ridge" parameter to control overfitting. The sum of squared weights is multiplied by this parameter before added to the squared error. Both classes use BFGS optimisation by default to find parameters that correspond to a local minimum of the error function. but optionally conjugated gradient descent is available, which can be faster for problems with many parameters. Logistic functions are used as the activation functions for all units apart from the output unit in MLPRegressor, which employs the identity function. Input attributes are standardised to zero mean and unit variance. MLPRegressor also rescales the target attribute (i.e., "class") using standardisation. All network parameters are initialised with small normally distributed random values.
Project URL	http://weka.sourceforge.net/doc.packages/multiLayerPerceptrons
Project Organization	University of Waikato, Hamilton, NZ
Source Code Management	https://svn.cms.waikato.ac.nz/svn/weka/tags/multiLayerPerceptrons-1.0.10

Download multiLayerPerceptrons 1.0.10

Filename	Size
multiLayerPerceptrons-1.0.10.pom
multiLayerPerceptrons-1.0.10.jar	44 KB
multiLayerPerceptrons-1.0.10-tests.jar	7 KB
multiLayerPerceptrons-1.0.10-test-sources.jar	4 KB
multiLayerPerceptrons-1.0.10-sources.jar	32 KB
multiLayerPerceptrons-1.0.10-javadoc.jar	127 KB

Apache Maven

<!-- https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/multiLayerPerceptrons/ -->
<dependency>
    <groupId>nz.ac.waikato.cms.weka</groupId>
    <artifactId>multiLayerPerceptrons</artifactId>
    <version>1.0.10</version>
</dependency>

Gradle Groovy

// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/multiLayerPerceptrons/
implementation 'nz.ac.waikato.cms.weka:multiLayerPerceptrons:1.0.10'

Gradle Kotlin

// https://jarcasting.com/artifacts/nz.ac.waikato.cms.weka/multiLayerPerceptrons/
implementation ("nz.ac.waikato.cms.weka:multiLayerPerceptrons:1.0.10")

Apache Buildr

'nz.ac.waikato.cms.weka:multiLayerPerceptrons:jar:1.0.10'

Apache Ivy

<dependency org="nz.ac.waikato.cms.weka" name="multiLayerPerceptrons" rev="1.0.10">
  <artifact name="multiLayerPerceptrons" type="jar" />
</dependency>

Groovy Grape

@Grapes(
@Grab(group='nz.ac.waikato.cms.weka', module='multiLayerPerceptrons', version='1.0.10')
)

Scala SBT

libraryDependencies += "nz.ac.waikato.cms.weka" % "multiLayerPerceptrons" % "1.0.10"

Leiningen

[nz.ac.waikato.cms.weka/multiLayerPerceptrons "1.0.10"]

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.