Package | Description |
---|---|
org.dllearner.algorithms.decisiontrees.refinementoperators | |
org.dllearner.algorithms.hybridgp |
Hybrid Learning Algorithm: A combination of Genetic Programming and Refinement Operators.
|
org.dllearner.algorithms.miles | |
org.dllearner.algorithms.ocel |
New experimental refinement operator approach, which takes
obtained information about concrete examples in an algorithm run
stronger into account.
|
org.dllearner.algorithms.qtl |
Learning algorithm based on so-called query trees
which are a tree based representation of a (set of) RDF resource(s)
|
org.dllearner.learningproblems |
Supported DL-Learner learning problems.
|
org.dllearner.refinementoperators |
Refinement operator implementations.
|
org.dllearner.utilities.split |
Modifier and Type | Method and Description |
---|---|
PosNegLP |
DLTreesRefinementOperator.getLp() |
Modifier and Type | Method and Description |
---|---|
void |
DLTreesRefinementOperator.setLp(PosNegLP lp) |
Constructor and Description |
---|
DLTreesRefinementOperator(PosNegLP lp,
AbstractReasonerComponent reasoner,
int beam) |
Constructor and Description |
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Psi(PosNegLP learningProblem,
AbstractReasonerComponent reasoningService) |
Constructor and Description |
---|
DescriptionLinearClassifier(PosNegLP lp,
AbstractReasonerComponent rc) |
MILES(AbstractCELA la,
PosNegLP lp,
AbstractReasonerComponent rc) |
Constructor and Description |
---|
OCEL(PosNegLP learningProblem,
AbstractReasonerComponent reasoningService) |
Constructor and Description |
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QTL2Disjunctive(PosNegLP learningProblem,
AbstractReasonerComponent reasoner) |
QTL2Disjunctive(PosNegLP lp,
org.aksw.jena_sparql_api.core.QueryExecutionFactory qef) |
QTL2Disjunctive(PosNegLP lp,
SparqlEndpointKS ks) |
QTL2DisjunctiveMultiThreaded(PosNegLP learningProblem,
AbstractReasonerComponent reasoner) |
QTL2DisjunctiveMultiThreaded(PosNegLP lp,
org.aksw.jena_sparql_api.core.QueryExecutionFactory qef) |
QTL2DisjunctiveMultiThreaded(PosNegLP lp,
SparqlEndpointKS ks) |
Modifier and Type | Class and Description |
---|---|
class |
PosNegLPStandard
The aim of this learning problem is to learn a concept definition such that
the positive examples and the negative examples do not follow.
|
class |
PosNegLPStrict |
class |
PosNegUndLP
A ternary learning problem (positive, negative and uncertain instances) to manage the problem of the Open World Assumption
typically employed for ontologies
|
Modifier and Type | Method and Description |
---|---|
PosNegLP |
ExampleLoader.getPosNegLP() |
PosNegLP |
PosNegUndLP.getPosNegLP()
A method for binarizing a ternary learning problem.
|
Modifier and Type | Method and Description |
---|---|
void |
ExampleLoader.setPosNegLP(PosNegLP posNegLP) |
Constructor and Description |
---|
PosNegLPStandard(PosNegLP lp)
Copy constructor
|
Constructor and Description |
---|
PsiDown(PosNegLP learningProblem,
AbstractReasonerComponent reasoningService) |
PsiUp(PosNegLP learningProblem,
AbstractReasonerComponent reasoningService) |
Constructor and Description |
---|
OptimizedNumericValuesSplitter(AbstractReasonerComponent reasoner,
org.semanticweb.owlapi.model.OWLDataFactory dataFactory,
PosNegLP lp) |
DL-Learner is licenced under the terms of the GNU General Public License.
Copyright © 2007-2019 Jens Lehmann