Package weka.classifiers.functions

package weka.classifiers.functions
  • Classes
    * Implements Gaussian processes for regression without hyperparameter-tuning.
    Class for using linear regression for prediction.
    Class for building and using a multinomial logistic regression model with a ridge estimator.

    There are some modifications, however, compared to the paper of leCessie and van Houwelingen(1992):

    If there are k classes for n instances with m attributes, the parameter matrix B to be calculated will be an m*(k-1) matrix.

    The probability for class j with the exception of the last class is

    Pj(Xi) = exp(XiBj)/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

    The last class has probability

    = 1/((sum[j=1..(k-1)]exp(Xi*Bj))+1)

    The (negative) multinomial log-likelihood is thus:

    L = -sum[i=1..n]{
    sum[j=1..(k-1)](Yij * ln(Pj(Xi)))
    +(1 - (sum[j=1..(k-1)]Yij))
    * ln(1 - sum[j=1..(k-1)]Pj(Xi))
    } + ridge * (B^2)

    In order to find the matrix B for which L is minimised, a Quasi-Newton Method is used to search for the optimized values of the m*(k-1) variables.
    A classifier that uses backpropagation to learn a multi-layer perceptron to classify instances.
    Implements stochastic gradient descent for learning various linear models (binary class SVM, binary class logistic regression, squared loss, Huber loss and epsilon-insensitive loss linear regression).
    Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
    Learns a simple linear regression model.
    Classifier for building linear logistic regression models.
    Implements John Platt's sequential minimal optimization algorithm for training a support vector classifier.

    This implementation globally replaces all missing values and transforms nominal attributes into binary ones.
    SMOreg implements the support vector machine for regression.
    Implementation of the voted perceptron algorithm by Freund and Schapire.