OpenML
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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Weka implementation of AttributeSelectedClassifier
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Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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Weka implementation of AttributeSelectedClassifier
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Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
40 runs0 likes0 downloads0 reach0 impact
Weka implementation of AttributeSelectedClassifier
40 runs0 likes0 downloads0 reach0 impact
Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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Weka implementation of AttributeSelectedClassifier
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Geoffrey I. Webb (2000). MultiBoosting: A Technique for Combining Boosting and Wagging. Machine Learning. Vol.40(No.2).
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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Weka implementation of MultiSearch
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D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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Yasser EL-Manzalawy (2005). WLSVM. URL http://www.cs.iastate.edu/~yasser/wlsvm/. Chih-Chung Chang, Chih-Jen Lin (2001). LIBSVM - A Library for Support Vector Machines. URL…
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Weka implementation of ZeroR
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Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA.
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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J. Platt: Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods - Support Vector Learning,…
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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J.H. Friedman (1999). Stochastic Gradient Boosting.
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
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Weka implementation of FilteredClassifier
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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Weka implementation of FilteredClassifier
126 runs0 likes0 downloads0 reach61 impact
Weka implementation of MultiSearch
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Learner mlr.regr.cforest from package(s) party.
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Learner mlr.regr.ctree from package(s) party.
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Learner mlr.regr.earth from package(s) earth.
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Learner mlr.regr.fnn from package(s) FNN.
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Learner mlr.regr.gbm from package(s) gbm.
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Learner mlr.regr.glmnet from package(s) glmnet.
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Learner mlr.regr.km from package(s) DiceKriging.
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Learner mlr.regr.ksvm from package(s) kernlab.
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Learner mlr.regr.lm from package(s) stats.
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Learner mlr.regr.nnet from package(s) nnet.
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Learner mlr.regr.pcr from package(s) pls.
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Learner mlr.regr.penalized.lasso from package(s) !penalized.
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Learner mlr.regr.penalized.ridge from package(s) !penalized.
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Learner mlr.regr.plsr from package(s) pls.
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Learner mlr.regr.randomForest from package(s) randomForest.
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Learner mlr.regr.rpart from package(s) rpart.
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Learner mlr.regr.rsm from package(s) rsm.
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Learner mlr.regr.rvm from package(s) kernlab.
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Weka implementation of FilteredClassifier
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Weka implementation of MultiSearch
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Code to run H2O through MLR. https://github.com/meta-QSAR/rmetaqsar/blob/master/fnc-deeplrn.R
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Learner mlr.regr.rvm from package(s) kernlab.
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Learner mlr.regr.rsm from package(s) rsm.
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Learner mlr.regr.rpart from package(s) rpart.
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Learner mlr.regr.randomForest from package(s) randomForest.
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Learner mlr.regr.penalized.ridge from package(s) !penalized.
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Flow generated by run_task
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Learner mlr.classif.naiveBayes from package(s) e1071.
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Learner mlr.classif.rpart from package(s) rpart.
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Learner mlr.classif.naiveBayes.preproc.preproc from package(s) e1071.
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Learner mlr.classif.naiveBayes.preproc from package(s) e1071.
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Learner mlr.classif.randomForest.preproc from package(s) randomForest.
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Learner mlr.classif.randomForest from package(s) randomForest.
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Learner mlr.classif.rpart.preproc.preproc from package(s) rpart.
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Learner mlr.classif.rpart.preproc from package(s) rpart.
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Learner mlr.classif.kknn.preproc.preproc from package(s) !kknn.
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Learner mlr.classif.kknn.preproc from package(s) !kknn.
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Learner mlr.classif.kknn from package(s) !kknn.
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Learner mlr.classif.mlp.preproc.preproc from package(s) RSNNS.
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Learner mlr.classif.mlp.preproc from package(s) RSNNS.
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Learner mlr.classif.mlp from package(s) RSNNS.
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flow for uploading results of the gridsearch for instrument *azure_two_class_boosted_decision_tree* on dataset *telescope*
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flow for uploading results of the gridsearch for instrument *azure_two_class_logistic_regression* on dataset *telescope*
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flow for uploading results of the gridsearch for instrument *azure_two_class_boosted_decision_tree* on dataset *telescope*
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Learner mlr.classif.PART.preproc.preproc from package(s) RWeka.
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