Documentation of 'jsat.classifiers.svm.extended.AMM' Java class
AMM
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jsat.classifiers.svm.extended

Class AMM

  • All Implemented Interfaces:
    java.io.Serializable, java.lang.Cloneable, Classifier, UpdateableClassifier, Parameterized

    public class AMM
extends OnlineAMM
    This is the batch variant of the Adaptive Multi-Hyperplane Machine (AMM) algorithm. It is related to linear SVMs where instead of having only a single weight vector, it is extended to multi-class problems by giving each class its own weight vector. It is further extended by allowing each class to dynamically add new weight vectors to increase the non-linearity of the solution.
    This algorithm works best for problems with a very large number of data points where traditional kernelized SVMs are prohibitively expensive to train due to computational cost.
    While the AMM trained in a batch setting can continue to be updated in an online fashion, the accuracy may reduce if done. This is because only the batch variant will reach a local optima.
    For this version the BaseUpdateableClassifier.setEpochs(int) method controls the total number of iterations of the learning algorithm. A small value in [5, 20] should be sufficient.
    See:
    • Wang, Z., Djuric, N., Crammer, K., & Vucetic, S. (2011). Trading representability for scalability Adaptive Multi-Hyperplane Machine for nonlinear Classification. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD ’11 (p. 24). New York, New York, USA: ACM Press. doi:10.1145/2020408.2020420
    • Djuric, N., Lan, L., Vucetic, S., & Wang, Z. (2014). BudgetedSVM: A Toolbox for Scalable SVM Approximations. Journal of Machine Learning Research, 14, 3813–3817. Retrieved from here
    See Also:
    Serialized Form

    • Constructor Detail

      • AMM

        public AMM()
        Creates a new batch AMM learner

      • AMM

        public AMM(double lambda)
        Creates a new batch AMM learner
        Parameters:
        lambda - the regularization value to use

      • AMM

        public AMM(double lambda,
           int classBudget)
        Creates a new batch AMM learner
        Parameters:
        lambda - the regularization value to use
        classBudget - the maximum number of weight vectors for each class

      • AMM

        public AMM(AMM toCopy)
        Copy constructor
        Parameters:
        toCopy - the object to copy

    • Method Detail

      • setSubEpochs

        public void setSubEpochs(int subEpochs)
        Each iteration of the batch AMM algorithm requires at least one epoch over the training set. This control how many epochs make up each iteration of training.
        Parameters:
        subEpochs - the number passes through the training set done on each iteration of training

      • getSubEpochs

        public int getSubEpochs()
        Returns the number of passes through the data set done on each iteration
        Returns:
        the number of passes through the data set done on each iteration

      • train

        public void train(ClassificationDataSet dataSet,
                  boolean parallel)
        Description copied from interface: Classifier
        Trains the classifier and constructs a model for classification using the given data set. If the training method knows how, it will used the threadPool to conduct training in parallel. This method will block until the training has completed.
        Specified by:
        train in interface Classifier
        Overrides:
        train in class BaseUpdateableClassifier
        Parameters:
        dataSet - the data set to train on
        parallel - true if multiple threads should be used to train the model. false if it should be done in a single threaded manner.
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