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weka.FilteredClassifier_CostSensitiveClassifier_Logistic (3)
Weka implementation of FilteredClassifier
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weka.RandomCommittee_RandomCommittee_RandomTree (1)
Weka implementation of RandomCommittee
1 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_AttributeSelectedClassifier_SMO_PolyKernel (1)
Weka implementation of RandomizableFilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_Vote_ZeroR (2)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_J48 (7)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.MultiClassClassifier_RandomTree (2)
Weka implementation of MultiClassClassifier
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weka.MultiClassClassifier_AdaBoostM1_RandomForest (1)
Weka implementation of MultiClassClassifier
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weka.CostSensitiveClassifier_SMO_PolyKernel (3)
Weka implementation of CostSensitiveClassifier
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weka.MetaCost_ZeroR (1)
Pedro Domingos: MetaCost: A general method for making classifiers cost-sensitive. In: Fifth International Conference on Knowledge Discovery and Data Mining, 155-164, 1999.
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weka.MultiBoostAB_RandomTree (4)
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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weka.RandomizableFilteredClassifier_RandomCommittee_RandomForest (1)
Weka implementation of RandomizableFilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_CostSensitiveClassifier_FilteredClassifier_RandomForest (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
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weka.CostSensitiveClassifier_FilteredClassifier_AttributeSelectedClassifier_RandomForest (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_FilteredClassifier_AttributeSelectedClassifier_SMO_PolyKernel (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_J48 (4)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_RandomForest (5)
Weka implementation of FilteredClassifier
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weka.FilteredClassifier_NaiveBayes (4)
Weka implementation of FilteredClassifier
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weka.CostSensitiveClassifier_CVParameterSelection_RandomForest (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_CostSensitiveClassifier_FilteredClassifier_RandomForest (1)
Weka implementation of FilteredClassifier
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weka.Vote_J48 (4)
Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
1 runs0 likes0 downloads0 reach0 impact
weka.MultiClassClassifier_CostSensitiveClassifier_REPTree (1)
Weka implementation of MultiClassClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_Vote_RandomizableFilteredClassifier_MultilayerPerceptron (1)
Weka implementation of RandomizableFilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_CostSensitiveClassifier_J48 (1)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach1 impact
weka.CostSensitiveClassifier_MultiClassClassifier_REPTree (1)
Weka implementation of CostSensitiveClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_AdaBoostM1_RandomTree (2)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_AdaBoostM1_CostSensitiveClassifier_ZeroR (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_AdaBoostM1_CostSensitiveClassifier_REPTree (1)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach0 impact
weka.FilteredClassifier_CostSensitiveClassifier_RandomTree (3)
Weka implementation of FilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.RandomizableFilteredClassifier_CostSensitiveClassifier_NaiveBayes (1)
Weka implementation of RandomizableFilteredClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.CostSensitiveClassifier_ZeroR (4)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_CostSensitiveClassifier_REPTree (1)
Weka implementation of CostSensitiveClassifier
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weka.RandomizableFilteredClassifier_Vote_RandomTree (1)
Weka implementation of RandomizableFilteredClassifier
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extra.neural_network.NeuralNetwork (1)
Automatically created scikit-learn flow.
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sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,featureunion=sklearn.pipeline.FeatureUnion(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures),votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier)) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,featureunion=sklearn.pipeline.FeatureUnion(functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer,polynomialfeatures=sklearn.preprocessing.data.PolynomialFeatures),votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.pipeline.Pipeline(robustscaler=sklearn.preprocessing.data.RobustScaler,extratreeclassifier=sklearn.tree.tree.ExtraTreeClassifier))) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.tree.tree.DecisionTreeClassifier,ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(DecisionTreeClassifier=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier),ExtraTreeClassifier=sklearn.tree.tree.ExtraTreeClassifier,KNN=sklearn.neighbors.classification.KNeighborsClassifier,RFC=sklearn.ensemble.forest.RandomForestClassifier)) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,svc=sklearn.svm.classes.SVC) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(featureunion=sklearn.pipeline.FeatureUnion(votingclassifier=sklearn.ensemble.voting_classifier.VotingClassifier(dtc=sklearn.tree.tree.DecisionTreeClassifier,etc=sklearn.tree.tree.ExtraTreeClassifier),functiontransformer=sklearn.preprocessing._function_transformer.FunctionTransformer),logisticregression=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
mlr.classif.svm (10)
Learner mlr.classif.svm from package(s) e1071.
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mlr.classif.ctree (12)
Learner mlr.classif.ctree from package(s) party.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,standardscaler=sklearn.preprocessing.data.StandardScaler,logisticregression=sklearn.linear_model.logistic.LogisticRegression) (2)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler,nusvc=sklearn.svm.classes.NuSVC) (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing.data.OneHotEncoder,standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
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sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.pipeline.Pipeline(dualimputer=extra.dual_imputer.DualImputer,onehotencoder=sklearn.preprocessing.data.OneHotEncoder,standardscaler=sklearn.preprocessing.data.StandardScaler,mlpclassifier=sklearn.neural_network.multilayer_perceptron.MLPClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
wrapper.optimus.Optimus (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.ensemble.forest.ExtraTreesClassifier) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
optimus.model_optimizer.ModelOptimizer(estimator=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
optimus.multi_optimizer.MultiOptimizer (1)
Automatically created scikit-learn flow.
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sklearn.pipeline.Pipeline(Imputer=sklearn.preprocessing.imputation.Imputer,OneHotEncoder=sklearn.preprocessing.data.OneHotEncoder,Classifier=sklearn.ensemble.forest.RandomForestClassifier) (2)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
sklearn.linear_model.logistic.LogisticRegression (4)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
optimus.model_optimizer2.ModelOptimizer(estimator=sklearn.linear_model.logistic.LogisticRegression) (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
mlr.classif.xgboost (8)
Learner mlr.classif.xgboost from package(s) xgboost.
1 runs0 likes1 downloads1 reach0 impact
mlr.classif.svm (11)
Learner mlr.classif.svm from package(s) e1071.
1 runs0 likes1 downloads1 reach0 impact
optimus.multi_optimizer2.MultiOptimizer (1)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomialText (2)
Weka implementation of NaiveBayesMultinomialText
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optimus.model_optimizer.ModelOptimizer(estimator=sklearn.linear_model.logistic.LogisticRegression) (3)
Automatically created scikit-learn flow.
1 runs0 likes0 downloads0 reach0 impact
mlr.classif.J48 (7)
Learner mlr.classif.J48 from package(s) RWeka.
1 runs0 likes0 downloads0 reach0 impact
optimus.model_optimizer.ModelOptimizer(estimator=sklearn.tree.tree.ExtraTreeClassifier) (1)
Automatically created scikit-learn flow.
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optimus.model_optimizer.ModelOptimizer(estimator=sklearn.ensemble.weight_boosting.AdaBoostClassifier(base_estimator=sklearn.tree.tree.DecisionTreeClassifier)) (1)
Automatically created scikit-learn flow.
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weka.AttributeSelectedClassifier_MultiBoostAB_IBk (2)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
mlr.classif.gausspr (1)
Learner mlr.classif.gausspr from package(s) kernlab.
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mlr.classif.rpart (40)
Learner mlr.classif.rpart from package(s) rpart.
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weka.Bagging_RandomTree (13)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_REPTree (2)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_RandomTree (2)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach1 impact
weka.AttributeSelectedClassifier_JRip (2)
Weka implementation of AttributeSelectedClassifier
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weka.AttributeSelectedClassifier_AdaBoostM1_REPTree (2)
Weka implementation of AttributeSelectedClassifier
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weka.AttributeSelectedClassifier_AdaBoostM1_LMT (2)
Weka implementation of AttributeSelectedClassifier
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mlr.classif.randomForest (32)
Learner mlr.classif.randomForest from package(s) randomForest.
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sklearn.svm.classes.SVC (9)
Automatically created scikit-learn flow.
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weka.AttributeSelectedClassifier_DecisionTable (1)
Weka implementation of AttributeSelectedClassifier
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sklearn.model_selection._search.GridSearchCV(estimator=sklearn.svm.classes.SVC) (1)
Automatically created scikit-learn flow.
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mlr.classif.rpart (41)
Learner mlr.classif.rpart from package(s) rpart.
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sklearn.neural_network.multilayer_perceptron.MLPClassifier (6)
Automatically created scikit-learn flow.
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sklearn.naive_bayes.BernoulliNB (3)
Automatically created scikit-learn flow.
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weka.RandomSubSpace_IBk (1)
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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weka.RandomSubSpace_Logistic (1)
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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weka.AttributeSelectedClassifier_LWL_Logistic (1)
Weka implementation of AttributeSelectedClassifier
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weka.RandomSubSpace_MultilayerPerceptron (1)
Tin Kam Ho (1998). The Random Subspace Method for Constructing Decision Forests. IEEE Transactions on Pattern Analysis and Machine Intelligence. 20(8):832-844. URL…
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weka.Vote_LMT (2)
Ludmila I. Kuncheva (2004). Combining Pattern Classifiers: Methods and Algorithms. John Wiley and Sons, Inc.. J. Kittler, M. Hatef, Robert P.W. Duin, J. Matas (1998). On combining classifiers. IEEE…
1 runs0 likes0 downloads0 reach0 impact
weka.LWL_ZeroR (1)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
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weka.AttributeSelectedClassifier_RandomCommittee_MultilayerPerceptron (1)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_LWL_MultilayerPerceptron (1)
Weka implementation of AttributeSelectedClassifier
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weka.AttributeSelectedClassifier_SMO_NormalizedPolyKernel (1)
Weka implementation of AttributeSelectedClassifier
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weka.AttributeSelectedClassifier_Vote_KStar (1)
Weka implementation of AttributeSelectedClassifier
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weka.LWL_NaiveBayes (2)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
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weka.AttributeSelectedClassifier_AdaBoostM1_RandomForest (1)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_Vote_RandomForest (1)
Weka implementation of AttributeSelectedClassifier
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weka.LWL_RandomForest (4)
Eibe Frank, Mark Hall, Bernhard Pfahringer: Locally Weighted Naive Bayes. In: 19th Conference in Uncertainty in Artificial Intelligence, 249-256, 2003. C. Atkeson, A. Moore, S. Schaal (1996). Locally…
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_AttributeSelectedClassifier_SMO_PolyKernel (1)
Weka implementation of AttributeSelectedClassifier
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weka.AdaBoostM1_JRip (4)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_LWL_SMO_PolyKernel (1)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_RandomSubSpace_Logistic (1)
Weka implementation of AttributeSelectedClassifier
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weka.Bagging_SMO_NormalizedPolyKernel (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.AttributeSelectedClassifier_Vote_MultilayerPerceptron (1)
Weka implementation of AttributeSelectedClassifier
1 runs0 likes0 downloads0 reach0 impact