Uses of Interface
weka.core.MultiInstanceCapabilitiesHandler

Packages that use MultiInstanceCapabilitiesHandler

weka.classifiers.mi
weka.classifiers.mi.supportVector
weka.filters.unsupervised.attribute

Uses of MultiInstanceCapabilitiesHandler in weka.classifiers.mi

Classes in weka.classifiers.mi that implement MultiInstanceCapabilitiesHandler

class	CitationKNN Modified version of the Citation kNN multi instance classifier. For more information see: Jun Wang, Zucker, Jean-Daniel: Solving Multiple-Instance Problem: A Lazy Learning Approach.
class	MDD Modified Diverse Density algorithm, with collective assumption. More information about DD: Oded Maron (1998).
class	MIBoost MI AdaBoost method, considers the geometric mean of posterior of instances inside a bag (arithmatic mean of log-posterior) and the expectation for a bag is taken inside the loss function. For more information about Adaboost, see: Yoav Freund, Robert E.
class	MIDD Re-implement the Diverse Density algorithm, changes the testing procedure. Oded Maron (1998).
class	MIEMDD EMDD model builds heavily upon Dietterich's Diverse Density (DD) algorithm. It is a general framework for MI learning of converting the MI problem to a single-instance setting using EM.
class	MILR Uses either standard or collective multi-instance assumption, but within linear regression.
class	MINND Multiple-Instance Nearest Neighbour with Distribution learner. It uses gradient descent to find the weight for each dimension of each exeamplar from the starting point of 1.0.
class	MIOptimalBall This classifier tries to find a suitable ball in the multiple-instance space, with a certain data point in the instance space as a ball center.
class	MISMO 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.
class	MISVM Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).
class	MIWrapper A simple Wrapper method for applying standard propositional learners to multi-instance data. For more information see: E.
class	SimpleMI Reduces MI data into mono-instance data.

Uses of MultiInstanceCapabilitiesHandler in weka.classifiers.mi.supportVector

Classes in weka.classifiers.mi.supportVector that implement MultiInstanceCapabilitiesHandler

class	MIPolyKernel The polynomial kernel : K(x, y) = <x, y>^p or K(x, y) = (<x, y>+1)^p Valid options are:
class	MIRBFKernel The RBF kernel.

Uses of MultiInstanceCapabilitiesHandler in weka.filters.unsupervised.attribute

Classes in weka.filters.unsupervised.attribute that implement MultiInstanceCapabilitiesHandler

class

MultiInstanceToPropositional Converts the multi-instance dataset into single instance dataset so that the Nominalize, Standardize and other type of filters or transformation can be applied to these data for the further preprocessing. Note: the first attribute of the converted dataset is a nominal attribute and refers to the bagId.