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.

Table Of Contents

• Latest Version
• All Versions
• View Java Class Source Code in JAR file

Latest Version

Download nz.ac.waikato.cms.weka : multiLayerPerceptrons JAR file - Latest Versions:

• Latest Stable: 1.0.10.jar

All Versions

Download nz.ac.waikato.cms.weka : multiLayerPerceptrons JAR file - All Versions:

Version		Vulnerabilities	Size	Updated
1.0.x	multiLayerPerceptrons-1.0.10.jar		44.88 KB	Oct 31, 2016
	multiLayerPerceptrons-1.0.9.jar		44.82 KB	Oct 14, 2016
	multiLayerPerceptrons-1.0.8.jar		44.71 KB	Nov 10, 2015
	multiLayerPerceptrons-1.0.7.jar		47.91 KB	Jul 15, 2014
	multiLayerPerceptrons-1.0.5.jar		47.61 KB	Sep 09, 2013
	multiLayerPerceptrons-1.0.4.jar		47.59 KB	Sep 07, 2013
	multiLayerPerceptrons-1.0.3.jar		47.59 KB	Sep 07, 2013
	multiLayerPerceptrons-1.0.2.jar		44.99 KB	Aug 27, 2013
	multiLayerPerceptrons-1.0.1.jar		30.06 KB	Nov 25, 2012
	multiLayerPerceptrons-1.0.0.jar		21.83 KB	Oct 23, 2012

View Java Class Source Code in JAR file

1. Download JD-GUI to open JAR file and explore Java source code file (.class .java)
2. Click menu "File → Open File..." or just drag-and-drop the JAR file in the JD-GUI window multiLayerPerceptrons-1.0.10.jar file.
   Once you open a JAR file, all the java classes in the JAR file will be displayed.

weka.classifiers.functions.activation

├─ weka.classifiers.functions.activation.ActivationFunction.class - [JAR]

├─ weka.classifiers.functions.activation.ApproximateSigmoid.class - [JAR]

├─ weka.classifiers.functions.activation.Sigmoid.class - [JAR]

├─ weka.classifiers.functions.activation.Softplus.class - [JAR]

weka.filters.unsupervised.attribute

├─ weka.filters.unsupervised.attribute.MLPAutoencoder.class - [JAR]

weka.classifiers.functions

├─ weka.classifiers.functions.MLPClassifier.class - [JAR]

├─ weka.classifiers.functions.MLPModel.class - [JAR]

├─ weka.classifiers.functions.MLPRegressor.class - [JAR]

weka.classifiers.functions.loss

├─ weka.classifiers.functions.loss.ApproximateAbsoluteError.class - [JAR]

├─ weka.classifiers.functions.loss.LossFunction.class - [JAR]

├─ weka.classifiers.functions.loss.SquaredError.class - [JAR]