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's flows

flow
weka.Bagging_LogitBoost_NaiveBayes (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_NaiveBayes (3)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.AdaBoostM1_NaiveBayes (4)
Yoav Freund, Robert E. Schapire: Experiments with a new boosting algorithm. In: Thirteenth International Conference on Machine Learning, San Francisco, 148-156, 1996.
2 runs0 likes0 downloads0 reach0 impact
weka.WeightedInstancesHandlerWrapper_NaiveBayes (1)
Weka implementation of WeightedInstancesHandlerWrapper
1 runs0 likes0 downloads0 reach0 impact
weka.Vote_NaiveBayes (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…
2 runs0 likes0 downloads0 reach0 impact
weka.Stacking_NaiveBayes (3)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
1 runs0 likes0 downloads0 reach0 impact
weka.Stacking_ZeroR (2)
David H. Wolpert (1992). Stacked generalization. Neural Networks. 5:241-259.
1 runs0 likes0 downloads0 reach0 impact
weka.RandomSubSpace_NaiveBayesUpdateable (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…
3 runs0 likes0 downloads0 reach0 impact
weka.RandomSubSpace_NaiveBayes (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…
3 runs0 likes0 downloads0 reach0 impact
weka.RandomCommittee_RandomTree (6)
Weka implementation of RandomCommittee
1 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_HoeffdingTree (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_HoeffdingTree (4)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
1 runs0 likes0 downloads0 reach0 impact
weka.IterativeClassifierOptimizer_AdditiveRegression_DecisionStump (3)
Weka implementation of IterativeClassifierOptimizer
0 runs0 likes0 downloads0 reach0 impact
weka.AdditiveRegression_DecisionStump (5)
J.H. Friedman (1999). Stochastic Gradient Boosting.
0 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_NaiveBayes (1)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
0 runs0 likes0 downloads0 reach0 impact
weka.ClassificationViaRegression_RandomForest (2)
E. Frank, Y. Wang, S. Inglis, G. Holmes, I.H. Witten (1998). Using model trees for classification. Machine Learning. 32(1):63-76.
1 runs0 likes0 downloads0 reach0 impact
weka.Bagging_NaiveBayesMultinomialUpdateable (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_NaiveBayesMultinomial (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
0 runs0 likes0 downloads0 reach0 impact
weka.Bagging_NaiveBayesUpdateable (1)
Leo Breiman (1996). Bagging predictors. Machine Learning. 24(2):123-140.
3 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_M5P (1)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_LMT (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.LogitBoost_HoeffdingTree (2)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.HoeffdingTree (5)
Geoff Hulten, Laurie Spencer, Pedro Domingos: Mining time-changing data streams. In: ACM SIGKDD Intl. Conf. on Knowledge Discovery and Data Mining, 97-106, 2001.
7 runs0 likes0 downloads0 reach3 impact
weka.LogitBoost_J48 (3)
J. Friedman, T. Hastie, R. Tibshirani (1998). Additive Logistic Regression: a Statistical View of Boosting. Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomialUpdateable (8)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
0 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_JRip (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.JRip (6)
William W. Cohen: Fast Effective Rule Induction. In: Twelfth International Conference on Machine Learning, 115-123, 1995.
0 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_PART (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
0 runs0 likes0 downloads0 reach0 impact
weka.PART (5)
Eibe Frank, Ian H. Witten: Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151, 1998.
0 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_OneR (1)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
1 runs0 likes0 downloads0 reach0 impact
weka.CVParameterSelection_ZeroR (5)
R. Kohavi (1995). Wrappers for Performance Enhancement and Oblivious Decision Graphs. Department of Computer Science, Stanford University.
1 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesMultinomial (5)
Andrew Mccallum, Kamal Nigam: A Comparison of Event Models for Naive Bayes Text Classification. In: AAAI-98 Workshop on 'Learning for Text Categorization', 1998.
0 runs0 likes0 downloads0 reach0 impact
weka.NaiveBayesUpdateable (11)
George H. John, Pat Langley: Estimating Continuous Distributions in Bayesian Classifiers. In: Eleventh Conference on Uncertainty in Artificial Intelligence, San Mateo, 338-345, 1995.
5 runs0 likes0 downloads0 reach0 impact
weka.Vote_ZeroR (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…
5 runs0 likes0 downloads0 reach0 impact