Uses of Interface
weka.core.Aggregateable

Packages that use Aggregateable

Package	Description
weka.classifiers.bayes
weka.classifiers.evaluation
weka.classifiers.functions
weka.classifiers.meta
weka.classifiers.trees
weka.estimators

Uses of Aggregateable in weka.classifiers.bayes

Classes in weka.classifiers.bayes that implement Aggregateable

Modifier and Type	Class and Description
class	NaiveBayes Class for a Naive Bayes classifier using estimator classes.
class	NaiveBayesMultinomialText Multinomial naive bayes for text data.
class	NaiveBayesUpdateable Class for a Naive Bayes classifier using estimator classes.

Uses of Aggregateable in weka.classifiers.evaluation

Classes in weka.classifiers.evaluation that implement Aggregateable

Modifier and Type	Class and Description
class	AggregateableEvaluation Subclass of Evaluation that provides a method for aggregating the results stored in another Evaluation object.

Uses of Aggregateable in weka.classifiers.functions

Classes in weka.classifiers.functions that implement Aggregateable

Modifier and Type	Class and Description
class	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.
class	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).
class	SGDText Implements stochastic gradient descent for learning a linear binary class SVM or binary class logistic regression on text data.

Uses of Aggregateable in weka.classifiers.meta

Classes in weka.classifiers.meta that implement Aggregateable

Modifier and Type	Class and Description
class	Bagging Class for bagging a classifier to reduce variance.
class	Vote Class for combining classifiers.

Uses of Aggregateable in weka.classifiers.trees

Classes in weka.classifiers.trees that implement Aggregateable

Modifier and Type	Class and Description
class	RandomForest Class for constructing a forest of random trees. For more information see: Leo Breiman (2001).

Uses of Aggregateable in weka.estimators

Classes in weka.estimators that implement Aggregateable

Modifier and Type	Class and Description
class	DiscreteEstimator Simple symbolic probability estimator based on symbol counts.
class	KernelEstimator Simple kernel density estimator.
class	NormalEstimator Simple probability estimator that places a single normal distribution over the observed values.