OpenML
Ron Kohavi: The Power of Decision Tables. In: 8th European Conference on Machine Learning, 174-189, 1995.
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Weka implementation of ConjunctiveRule
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Leland Wilkinson, Anushka Anand,, Tuan Dang : CHIRP: A new classi?er based on Composite Hypercubes on Iterated Random Projection. In: Proceedings of the 17th ACM KDD, 2011.
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Weka implementation of HyperPipes
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Weka implementation of InputMappedClassifier
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S. Lievens, B. De Baets, K. Cao-Van (2006). A Probabilistic Framework for the Design of Instance-Based Supervised Ranking Algorithms in an Ordinal Setting. Annals of Operations Research.. Kim Cao-Van…
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G. Demiroz, A. Guvenir: Classification by voting feature intervals. In: 9th European Conference on Machine Learning, 85-92, 1997.
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D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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John G. Cleary, Leonard E. Trigg: K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114, 1995.
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Zijian Zheng, G. Webb (2000). Lazy Learning of Bayesian Rules. Machine Learning. 4(1):53-84.
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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 implementation of KernelLogisticRegression
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Rong-En Fan, Kai-Wei Chang, Cho-Jui Hsieh, Xiang-Rui Wang, Chih-Jen Lin (2008). LIBLINEAR - A Library for Large Linear Classification. URL http://www.csie.ntu.edu.tw/~cjlin/liblinear/.
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le Cessie, S., van Houwelingen, J.C. (1992). Ridge Estimators in Logistic Regression. Applied Statistics. 41(1):191-201.
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Weka implementation of MultilayerPerceptron
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Weka implementation of MultilayerPerceptronCS
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Weka implementation of RBFClassifier
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Weka implementation of SGD
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Moa implementation of ADACC
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Moa implementation of LeveragingBag
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Weka implementation of FilteredClassifier
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Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive…
1039 runs0 likes2 downloads2 reach55 impact
H. Zhang, L. Jiang, J. Su: Hidden Naive Bayes. In: Twentieth National Conference on Artificial Intelligence, 919-924, 2005.
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Weka implementation of KernelLogisticRegression
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Niels Landwehr, Mark Hall, Eibe Frank (2005). Logistic Model Trees. Marc Sumner, Eibe Frank, Mark Hall: Speeding up Logistic Model Tree Induction. In: 9th European Conference on Principles and…
1207 runs0 likes2 downloads2 reach82 impact
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,…
1208 runs0 likes2 downloads2 reach5 impact
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,…
1206 runs0 likes2 downloads2 reach20 impact
S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
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N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318. N. Littlestone (1989). Mistake bounds and logarithmic…
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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 implementation of AttributeSelectedClassifier
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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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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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Juan J. Rodriguez, Ludmila I. Kuncheva, Carlos J. Alonso (2006). Rotation Forest: A new classifier ensemble method. IEEE Transactions on Pattern Analysis and Machine Intelligence. 28(10):1619-1630.…
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Jens Christian Huehn, Eyke Huellermeier (2009). FURIA: An Algorithm for Unordered Fuzzy Rule Induction. Data Mining and Knowledge Discovery..
1161 runs0 likes2 downloads2 reach34 impact
Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.
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Learner surv.coxph from package(s) survival.
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The performanceEstimation standardWF using svm as the learner
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Learner classif.randomForest from package(s) randomForest.
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Moa implementation of OzaBag
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Learner classif.rpart from package(s) rpart.
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Learner classif.lda from package(s) MASS.
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Learner classif.rpart from package(s) rpart.
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Learner classif.logreg from package(s) stats.
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Learner classif.rpart from package(s) rpart.
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Learner classif.randomForest from package(s) randomForest.
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A RapidMiner Workflow.
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R.C. Holte (1993). Very simple classification rules perform well on most commonly used datasets. Machine Learning. 11:63-91.
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Learner classif.rpart from package(s) rpart.
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Ross J. Quinlan: Learning with Continuous Classes. In: 5th Australian Joint Conference on Artificial Intelligence, Singapore, 343-348, 1992. Y. Wang, I. H. Witten: Induction of model trees for…
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Geoffrey Holmes, Mark Hall, Eibe Frank: Generating Rule Sets from Model Trees. In: Twelfth Australian Joint Conference on Artificial Intelligence, 1-12, 1999. Ross J. Quinlan: Learning with Continuous…
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Learner classif.avNNet from package(s) nnet.
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Learner classif.ada from package(s) ada.
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Learner classif.binomial from package(s) stats.
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Learner classif.randomForest from package(s) randomForest.
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George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
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Weka implementation of SGD
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Learner classif.rpart from package(s) rpart.
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Y. Freund, R. E. Schapire: Large margin classification using the perceptron algorithm. In: 11th Annual Conference on Computational Learning Theory, New York, NY, 209-217, 1998.
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No description
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David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
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Weka implementation of AttributeSelectedClassifier
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Weka implementation of AttributeSelectedClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Learner classif.gbm from package(s) gbm.
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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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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of FilteredClassifier
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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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Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive…
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Webb, Geoffrey I., Boughton, Janice, Zheng, Fei, Ting, Kai Ming, Salem, Houssam (2012). Learning by extrapolation from marginal to full-multivariate probability distributions: decreasingly naive…
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Weka implementation of KernelLogisticRegression
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Weka implementation of KernelLogisticRegression
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Weka implementation of RBFNetwork
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S. Shalev-Shwartz, Y. Singer, N. Srebro: Pegasos: Primal Estimated sub-GrAdient SOlver for SVM. In: 24th International Conference on MachineLearning, 807-814, 2007.
210 runs0 likes2 downloads2 reach58 impact
N. Littlestone (1988). Learning quickly when irrelevant attributes are abound: A new linear threshold algorithm. Machine Learning. 2:285-318. N. Littlestone (1989). Mistake bounds and logarithmic…
46 runs0 likes2 downloads2 reach37 impact
D. Aha, D. Kibler (1991). Instance-based learning algorithms. Machine Learning. 6:37-66.
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Weka implementation of HyperPipes
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Weka implementation of Ridor
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Jens Christian Huehn, Eyke Huellermeier (2009). FURIA: An Algorithm for Unordered Fuzzy Rule Induction. Data Mining and Knowledge Discovery..
270 runs0 likes2 downloads2 reach35 impact
Mark Hall, Eibe Frank: Combining Naive Bayes and Decision Tables. In: Proceedings of the 21st Florida Artificial Intelligence Society Conference (FLAIRS), 318-319, 2008.
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Weka implementation of ConjunctiveRule
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Leo Breiman, Jerome H. Friedman, Richard A. Olshen, Charles J. Stone (1984). Classification and Regression Trees. Wadsworth International Group, Belmont, California.
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Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid. In: Second International Conference on Knoledge Discovery and Data Mining, 202-207, 1996.
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Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall: Multiclass alternating decision trees. In: ECML, 161-172, 2001.
308 runs0 likes2 downloads2 reach17 impact
Haijian Shi (2007). Best-first decision tree learning. Hamilton, NZ. Jerome Friedman, Trevor Hastie, Robert Tibshirani (2000). Additive logistic regression : A statistical view of boosting. Annals of…
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Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
1196 runs0 likes2 downloads2 reach671 impact
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
298 runs0 likes2 downloads2 reach31 impact
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
268 runs0 likes2 downloads2 reach55 impact
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
175 runs0 likes2 downloads2 reach37 impact
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Weka implementation of CostSensitiveClassifier
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Weka implementation of CostSensitiveClassifier
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Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
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Learner classif.gbm from package(s) gbm.
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Learner classif.rpart from package(s) rpart.
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Learner classif.svm from package(s) e1071.
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Learner classif.glmboost from package(s) mboost.
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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,…
137 runs0 likes2 downloads2 reach118 impact