Uses of Package
weka.classifiers.functions

Packages that use weka.classifiers.functions

Package	Description
weka.classifiers.functions
weka.classifiers.functions.supportVector
weka.classifiers.trees.lmt

Classes in weka.classifiers.functions used by weka.classifiers.functions

Class and Description
Logistic 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)]Pj(Xi)) = 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.
SGD 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).
SGDText Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.
SGDText.Count

Classes in weka.classifiers.functions used by weka.classifiers.functions.supportVector

Class and Description
SMOreg SMOreg implements the support vector machine for regression.

Classes in weka.classifiers.functions used by weka.classifiers.trees.lmt

Class and Description
SimpleLinearRegression Learns a simple linear regression model.