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regr.rpart (1)
Learner regr.rpart from package(s) rpart.
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regr.lm (1)
Learner regr.lm from package(s) stats.
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R.3.1.2.classif.svm (3)
The performanceEstimation standardWF using svm as the learner
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classif.J48 (2)
Learner classif.J48 from package(s) RWeka.
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regr.randomForest (1)
Learner regr.randomForest from package(s) randomForest.
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moa.WEKAClassifier_LinearRegression (2)
Moa implementation of WEKAClassifier
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moa.WEKAClassifier_Winnow (1)
Moa implementation of WEKAClassifier
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moa.WEKAClassifier_Ridor (1)
Moa implementation of WEKAClassifier
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R.3.1.2.classif.qda (1)
The performanceEstimation standardWF using qda as the learner
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weka.A1DE (5)
G. Webb, J. Boughton, Z. Wang (2005). Not So Naive Bayes: Aggregating One-Dependence Estimators. Machine Learning. 58(1):5-24.
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weka.Discretize (3)
Usama M. Fayyad, Keki B. Irani: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence,…
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weka.PrincipalComponents (13)
Weka implementation of PrincipalComponents
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weka.REPTree (10)
Weka implementation of REPTree
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weka.REPTree (11)
Weka implementation of REPTree
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weka.REPTree (12)
Weka implementation of REPTree
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weka.REPTree (13)
Weka implementation of REPTree
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weka.K2 (6)
G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing Bayesian belief networks from databases. G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of probabilistic…
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weka.BayesNet_K2 (13)
Weka implementation of BayesNet
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weka.K2 (7)
G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing Bayesian belief networks from databases. G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of probabilistic…
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weka.K2 (8)
G.F. Cooper, E. Herskovits (1990). A Bayesian method for constructing Bayesian belief networks from databases. G. Cooper, E. Herskovits (1992). A Bayesian method for the induction of probabilistic…
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classif.J48 (4)
Learner classif.J48 from package(s) RWeka.
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classif.J48 (6)
Learner classif.J48 from package(s) RWeka.
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classif.J48 (8)
Learner classif.J48 from package(s) RWeka.
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classif.sda (1)
Learner classif.sda from package(s) sda.
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classif.sda (2)
Learner classif.sda from package(s) sda.
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weka.FilteredClassifier_MathExpression_AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_NaiveBayes (1)
Weka implementation of FilteredClassifier
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weka.MathExpression (1)
Weka implementation of MathExpression
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weka.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_NaiveBayes (2)
Weka implementation of AttributeSelectedClassifier
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classif.J48 (9)
Learner classif.J48 from package(s) RWeka.
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weka.FilteredClassifier_StratifiedRemoveFolds_NaiveBayes (1)
Weka implementation of FilteredClassifier
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weka.StratifiedRemoveFolds (1)
Weka implementation of StratifiedRemoveFolds
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classif.J48 (10)
Learner classif.J48 from package(s) RWeka.
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weka.ADTree (1)
Freund, Y., Mason, L.: The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133, 1999.
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classif.J48 (11)
Learner classif.J48 from package(s) RWeka.
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classif.J48 (12)
Learner classif.J48 from package(s) RWeka.
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classif.J48 (13)
Learner classif.J48 from package(s) RWeka.
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classif.J48 (14)
Learner classif.J48 from package(s) RWeka.
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classif.rpart (28)
Learner classif.rpart from package(s) rpart.
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weka.NaiveBayes (11)
No description
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weka.J48 (30)
No description
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weka.IBk (5)
No description
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weka.RandomForest (7)
No description
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weka.NaiveBayes (12)
No description
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weka.J48 (31)
No description
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weka.IBk (6)
No description
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classif.rpart (30)
Learner classif.rpart from package(s) rpart.
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rm.operator.parallel_decision_tree (2)
A RapidMiner Operator
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rm.operator.bagging (1)
A RapidMiner Operator
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weka.CostSensitiveClassifier_J48 (3)
Weka implementation of CostSensitiveClassifier
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weka.LinearRegression (6)
Weka implementation of LinearRegression
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weka.LogitBoost_LogitBoost_REPTree (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.Vote_CostSensitiveClassifier_PART (1)
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…
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weka.CostSensitiveClassifier_Vote_REPTree (1)
Weka implementation of CostSensitiveClassifier
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weka.Vote_REPTree (3)
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…
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weka.Vote_CostSensitiveClassifier_REPTree (1)
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…
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weka.LogitBoost_CostSensitiveClassifier_AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48 (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.CfsSubsetEval (5)
M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.
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weka.BestFirst (4)
Weka implementation of BestFirst
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weka.LogitBoost_AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48 (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.AttributeSelectedClassifier_CfsSubsetEval_BestFirst_J48 (5)
Weka implementation of AttributeSelectedClassifier
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weka.LogitBoost_AttributeSelectedClassifier_CfsSubsetEval_BestFirst_REPTree (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.Vote_CostSensitiveClassifier_Bagging_REPTree (1)
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…
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weka.LogitBoost_REPTree (5)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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weka.AdditiveRegression_DecisionStump (4)
J.H. Friedman (1999). Stochastic Gradient Boosting.
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weka.CostSensitiveClassifier_RandomSubSpace_REPTree (2)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_M5P (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.WrapperSubsetEval_ZeroR (2)
Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324.
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weka.CostSensitiveClassifier_REPTree (5)
Weka implementation of CostSensitiveClassifier
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weka.GaussianProcesses_PolyKernel (9)
David J.C. Mackay (1998). Introduction to Gaussian Processes. Dept. of Physics, Cambridge University, UK.
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weka.PolyKernel (9)
Weka implementation of PolyKernel
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weka.CostSensitiveClassifier_RandomCommittee_REPTree (1)
Weka implementation of CostSensitiveClassifier
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weka.RandomCommittee_REPTree (1)
Weka implementation of RandomCommittee
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weka.AttributeSelectedClassifier_WrapperSubsetEval_ZeroR_BestFirst_CostSensitiveClassifier_AdaBoostM1_REPTree (1)
Weka implementation of AttributeSelectedClassifier
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weka.WrapperSubsetEval_ZeroR (3)
Ron Kohavi, George H. John (1997). Wrappers for feature subset selection. Artificial Intelligence. 97(1-2):273-324.
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weka.CostSensitiveClassifier_AdaBoostM1_REPTree (2)
Weka implementation of CostSensitiveClassifier
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weka.MetaCost_REPTree (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.RandomSubSpace_REPTree (7)
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.Bagging_CostSensitiveClassifier_AdaBoostM1_REPTree (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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weka.CostSensitiveClassifier_AdaBoostM1_REPTree (3)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_REPTree (5)
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.RandomCommittee_CostSensitiveClassifier_RandomTree (1)
Weka implementation of RandomCommittee
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weka.PrincipalComponents (14)
Weka implementation of PrincipalComponents
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weka.Ranker (3)
Weka implementation of Ranker
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weka.CostSensitiveClassifier_AdaBoostM1_REPTree (4)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_REPTree (6)
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_AdaBoostM1_REPTree (5)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_REPTree (7)
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.GreedyStepwise (2)
Weka implementation of GreedyStepwise
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weka.CostSensitiveClassifier_AdaBoostM1_REPTree (6)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_REPTree (8)
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.ReliefFAttributeEval (1)
Kenji Kira, Larry A. Rendell: A Practical Approach to Feature Selection. In: Ninth International Workshop on Machine Learning, 249-256, 1992. Igor Kononenko: Estimating Attributes: Analysis and…
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weka.CostSensitiveClassifier_AdaBoostM1_REPTree (7)
Weka implementation of CostSensitiveClassifier
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weka.AdaBoostM1_REPTree (9)
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.AttributeSelectedClassifier_InfoGainAttributeEval_Ranker_CostSensitiveClassifier (1)
Weka implementation of AttributeSelectedClassifier
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weka.InfoGainAttributeEval (2)
Weka implementation of InfoGainAttributeEval
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weka.CostSensitiveClassifier (1)
Weka implementation of CostSensitiveClassifier
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weka.CostSensitiveClassifier_Bagging_JRip (1)
Weka implementation of CostSensitiveClassifier
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weka.AttributeSelectedClassifier_GainRatioAttributeEval_Ranker_CostSensitiveClassifier (1)
Weka implementation of AttributeSelectedClassifier
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weka.GainRatioAttributeEval (1)
Weka implementation of GainRatioAttributeEval
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weka.Bagging_AdaBoostM1_REPTree (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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