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Package

weka.classifiers.functions

Synopsis

Implements the stochastic variant of the Pegasos (Primal Estimated sub-GrAdient SOlver for SVM) method of Shalev-Shwartz et al. (2007). This implementation globally replaces all missing values and transforms nominal attributes into binary ones. It also normalizes all attributes, so the coefficients in the output are based on the normalized data. Can either minimize the hinge loss (SVM) or log loss (logistic regression). This implementation can be trained incrementally on (potentially) infinite data streams. For more information, see

S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.

Available in Weka 3.6.x - 3.7.1. Available via the package management system for Weka >= 3.7.2 (SPegasos).

Options

The table below describes the options available for SPegasos.

Option

Description

debug

If set to true, classifier may output additional info to the console.

dontNormalize

Turn normalization off

dontReplaceMissing

Turn off global replacement of missing values

epochs

The number of epochs to perform (batch learning). The total number of iterations is epochs * num instances.

lambda

The regularization constant. (default = 0.0001)

lossFunction

The loss function to use. Hinge loss (SVM) or log loss (logistic regression).

Capabilities

The table below describes the capabilites of SPegasos.

Capability

Supported

Class

Binary class, Missing class values

Attributes

Binary attributes, Unary attributes, Numeric attributes, Missing values, Empty nominal attributes, Nominal attributes

Min # of instances

0

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