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weka.MultilayerPerceptronCS

weka.MultilayerPerceptronCS

Visibility: public Uploaded 05-02-2015 by Jan van Rijn Weka_3.7.12 227 runs
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  • Verified_Learning_Curve,Verified_Supervised_Classification weka weka_3.7.12
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Weka implementation of MultilayerPerceptronCS

Parameters

AAutocreation of the network connections will NOT be done. (This will be ignored if -G is NOT set)
BA NominalToBinary filter will NOT automatically be used. (Set this to not use a NominalToBinary filter).
CNormalizing a numeric class will NOT be done. (Set this to not normalize the class if it's numeric).
DLearning rate decay will occur. (Set this to cause the learning rate to decay).
EThe consequetive number of errors allowed for validation testing before the netwrok terminates. (Value should be > 0, Default = 20).default: 20
GGUI will be opened. (Use this to bring up a GUI).
HThe hidden layers to be created for the network. (Value should be a list of comma separated Natural numbers or the letters 'a' = (attribs + classes) / 2, 'i' = attribs, 'o' = classes, 't' = attribs .+ classes) for wildcard values, Default = a).default: a
INormalizing the attributes will NOT be done. (Set this to not normalize the attributes).
LLearning Rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.3).default: 0.3
MMomentum Rate for the backpropagation algorithm. (Value should be between 0 - 1, Default = 0.2).default: 0.2
NNumber of epochs to train through. (Default = 500).default: 500
RReseting the network will NOT be allowed. (Set this to not allow the network to reset).
SThe value used to seed the random number generator (Value should be >= 0 and and a long, Default = 0).default: 0
VPercentage size of validation set to use to terminate training (if this is non zero it can pre-empt num of epochs. (Value should be between 0 - 100, Default = 0).default: 0
secondary-trainingSecondary task training set to use, as drawn from the data source file.
validation-setValidation set to use, as drawn from the data source file.

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