Package com.rapidminer.operator.learner.functions.kernel

Learning schemes which make use of kernel functions to transform the feature space, e.g. support vector machines.

See:
Description

Class Summary

AbstractKernelBasedLearner	This class is the common super class of all KernelModel producing learners.
AbstractMySVMLearner	This is the abstract superclass for the support vector machine / KLR implementations of Stefan Rüping.
AbstractMySVMModel	The abstract superclass for the SVM models by Stefan Rueping.
GPLearner	Gaussian Process (GP) Learner.
GPModel	A model learned by the GPLearner.
JMySVMLearner	This learner uses the Java implementation of the support vector machine mySVM by Stefan Rüping.
JMySVMModel	The implementation for the mySVM model (Java version) by Stefan Rueping.
KernelLogisticRegression	This operator determines a logistic regression model.
KernelLogisticRegressionModel	The model determined by the KernelLogisticRegression operator.
KernelLogisticRegressionOptimization	Evolutionary Strategy approach for optimization of the logistic regression problem.
KernelModel	This is the abstract model class for all kernel models.
LibSVMLearner	Applies the libsvm learner by Chih-Chung Chang and Chih-Jen Lin.
LibSVMModel	A model generated by the libsvm by Chih-Chung Chang and Chih-Jen Lin.
LinearMySVMLearner	This class implements a special case of the MySVM by restricting it to the linear (dot) kernel.
LinearMySVMModel	An optimized implementation for Linear MySVM Models that only store the coefficients to save memory and apply these weights directly without kernel transformations.
MyKLRLearner	This is the Java implementation of myKLR by Stefan Rüping.
MyKLRModel	The model for the MyKLR learner by Stefan Rueping.
RVMLearner	Relevance Vector Machine (RVM) Learner.
RVMModel	A model generated by the RVMLearner.
SupportVector	Holds all information of a support vector, i.e. the attribute values, the label, and the alpha.

Package com.rapidminer.operator.learner.functions.kernel Description

Learning schemes which make use of kernel functions to transform the feature space, e.g. support vector machines.