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See:
Description
| Interface Summary | |
|---|---|
| ExampleBasedHeuristic | Marker interface for heuristics in the refinement operator based learning approach. |
| Class Summary | |
|---|---|
| ExampleBasedNode | Represents a node in the search tree. |
| FlexibleHeuristic | This heuristic compares two nodes by computing a score using the number of covered negatives and the horizontal expansion factor of a node as input. |
| LexicographicHeuristic | |
| MultiHeuristic | This heuristic combines the following criteria to assign a double score value to a node: quality/accuracy of a concept (based on the full training set, not the negative example coverage as the flexible heuristic) horizontal expansion accuracy gain: The heuristic takes into account the accuracy difference between a node and its parent. |
| NodeComparatorStable | This comparator is stable, because it only takes covered examples, concept length and the concepts itself (using again a stable comparator) into account, which do not change during the run of the algorithm. |
| ROLComponent2 | The DL-Learner learning algorithm component for the example based refinement operator approach. |
| ROLearner2 | Implements the 2nd version of the refinement operator based learning approach. |
| SubsumptionComparator | |
| Enum Summary | |
|---|---|
| ExampleBasedNode.QualityEvaluationMethod | |
New experimental refinement operator approach, which takes obtained information about concrete examples in an algorithm run stronger into account.
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