程序包 weka.classifiers.mi
package weka.classifiers.mi
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类类说明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.Modified Diverse Density algorithm, with collective assumption.
More information about DD:
Oded Maron (1998).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.Re-implement the Diverse Density algorithm, changes the testing procedure.
Oded Maron (1998).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.Uses either standard or collective multi-instance assumption, but within linear regression.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.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.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.Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL).A simple Wrapper method for applying standard propositional learners to multi-instance data.
For more information see:
E.Reduces MI data into mono-instance data.