SVM Multi-class Classification
Multiclass SVM aims to assign labels to instances by using support vector machines, where the labels are drawn from a finite set of several elements.
The implemented approach for doing so is to reduce the single multiclass problem into multiple binary classification problems via one-versus-all.
The one-versus-all approach is the process of building binary classifiers which distinguish between one of the labels and the rest.
Model
The model keeps the pairs <ClassLabel, SVMLinearBinaryClassificationModel> and it enables a prediction to be made for a given vector of features, in the following way:
SVMLinearMultiClassClassificationModel model = ...;
double prediction = model.predict(observation);Trainer
Presently, Ignite supports the following parameters for SVMLinearMultiClassClassificationTrainer:
-
amountOfIterations- amount of outer SDCA algorithm iterations. (default value:200) -
amountOfLocIterations- amount of local SDCA algorithm iterations. (default value:100) -
lambda- regularization parameter (default value:0.4)
All properties will be propagated for each pair one-versus-all SVMLinearBinaryClassificationTrainer.
// Set up the trainer
SVMLinearMultiClassClassificationTrainer trainer = new SVMLinearMultiClassClassificationTrainer()
.withAmountOfIterations(AMOUNT_OF_ITERATIONS)
.withAmountOfLocIterations(AMOUNT_OF_LOC_ITERATIONS)
.withLambda(LAMBDA);
// Build the model
SVMLinearMultiClassClassificationModel mdl = trainer.fit(
datasetBuilder,
featureExtractor,
labelExtractor
);© 2025 GridGain Systems, Inc. All Rights Reserved. Privacy Policy | Legal Notices. GridGain® is a registered trademark of GridGain Systems, Inc.
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