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| Class Summary | |
|---|---|
| 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. |
| MDD | Modified Diverse Density algorithm, with collective assumption. More information about DD: Oded Maron (1998). |
| 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. |
| MIDD | Re-implement the Diverse Density algorithm, changes the testing procedure. Oded Maron (1998). |
| 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. |
| MILR | Uses either standard or collective multi-instance assumption, but within linear regression. |
| 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. |
| 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. |
| 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. |
| MISVM | Implements Stuart Andrews' mi_SVM (Maximum pattern Margin Formulation of MIL). |
| MIWrapper | A simple Wrapper method for applying standard propositional learners to multi-instance data. For more information see: E. |
| SimpleMI | Reduces MI data into mono-instance data. |
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