VotedPerceptron (3.6)

Implementation of the voted perceptron algorithm by Freund and Schapire. Globally replaces all missing values, and transforms nominal attributes into binary ones. For more information, see: Y. Freund, R. E. Schapire: Large margin classification using the perceptron algorithm. In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify classifier-specific parameters.


Class column
Choose the column that contains the target variable.
Preliminary Attribute Check

The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.

Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.

Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, Missing values, Binary class, Missing class values] Dependencies: [] min # Instance: 0

Classifier Options

I: The number of iterations to be performed. (default 1)

E: The exponent for the polynomial kernel. (default 1)

S: The seed for the random number generation. (default 1)

M: The maximum number of alterations allowed. (default 10000)

Input Ports

Training data

Output Ports

Trained classifier

Popular Predecessors

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Popular Successors

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Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.


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