155 citations found. Retrieving documents...
Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36(1-2):105--142.

 Home/Search   Document Details and Download   Summary   Related Articles   Check  

This paper is cited in the following contexts:

First 50 documents  Next 50

On a Unified Framework for Sampling with and.. - Martinez-Otzeta..   (Correct)

No context found.

Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning, 36(1-2):105--142.


Boosting Lazy Decision Trees - Xiaoli Zhang Fern   (Correct)

No context found.

Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36, 105-- 139.


Analyzing Bagging - Peter Buhlmann Eth (2001)   (2 citations)  (Correct)

No context found.

Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning 36, 105--139.


Competent Undemocratic Committees - Duch, Itert, Grudzinski (2002)   (1 citation)  (Correct)

No context found.

Bauer E, Kohavi R. (1999) An empirical comparison of voting classification algorithms: Bagging, Boosting and variants. Machine Learning 36, 105-139


Competent Undemocratic Committees - Duch, Itert, Grudzinski (2002)   (1 citation)  (Correct)

No context found.

Bauer E, Kohavi R. (1999) An empirical comparison of voting classification algorithms: bagging, boosting and variants. Machine learning 36, 105-142


Bias and Variance of Rotation-based Ensembles - Juan Jose Rodrguez   (Correct)

No context found.

Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1--2):105--139, 1999.


An Empirical Comparison of Boosting Methods - Via Oaidtb An   (Correct)

No context found.

Eric Bauer and Ron Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1--2):105--139, 1999.


The Effect Of Small Disjuncts And - Class Distribution On   (Correct)

No context found.

Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Machine Learning, 36, 105-139.


An Empirical Evaluation of Supervised Learning - For Roc Area   (Correct)

No context found.

Eric Bauer and Ron Kohavi, `An empirical comparison of voting classification algorithms: Bagging, boosting, and variants', Machine Learning, 36(1-2), (1999).


An Empirical Evaluation of Supervised Learning for ROC Area - Caruana, Niculescu-Mizil (2004)   (Correct)

No context found.

Eric Bauer and Ron Kohavi, `An empirical comparison of voting classification algorithms: Bagging, boosting, and variants', Machine Learning, 36(1-2), (1999).


A Smoothed Boosting Algorithm Using Probabilistic Output Codes - Jin, Zhang (2005)   (Correct)

No context found.

Bauer, E., & Kohavi, R. (1999). An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting and Variants. Machine Learning, 36, 105--139.


Switching Class Labels to Generate Classification Ensembles - Martínez-Muñoz, Suárez (2005)   (Correct)

No context found.

E. Bauer, R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning 36 (1-2) (1999) 105--139.


Aggregation Ordering in Bagging - Martínez-Muñoz, Suárez (2004)   (Correct)

No context found.

E. Bauer and R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning, 36(1-2), 1999, 105-- 139


Using Boosting to Prune Bagging Ensembles - Suarez   (Correct)

No context found.

Bauer, E., Kohavi, R., 1999. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 (1-2), 105--139.


Learning with Class Skews and Small Disjuncts - Ronaldo Prati Gustavo (2004)   (Correct)

No context found.

Bauer, E., Kohavi, R.: An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36 (1999) 105--139


A Study of the Behavior of Several Methods for Balancing.. - Batista, Prati, Monard (2004)   (Correct)

No context found.

Bauer, E., and Kohavi, R. An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36 (1999), 105--139.


Barrier Boosting - Rätsch, Warmuth, Mika, Onoda, Müller (2000)   (Correct)

No context found.

E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithm: Bagging, boosting and variants. Machine Learning, pages 105--142, 1999.


On The Size of Training Set and - The Benefit From   (Correct)

No context found.

Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants. Machine Learning 36 (1999) 105--139


Ensembling MML Causal Discovery - Dai, Li, Zhou   (Correct)

No context found.

Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. machine learning 36 (1999) 105--139


Combining Multiple Clustering Systems - Boulis, Ostendorf   (Correct)

No context found.

Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36 (1999) 105--139


Barrier Boosting - Rätsch, Warmuth, Mika, Onoda, Lemm..   (Correct)

No context found.

E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithm: Bagging, boosting and variants. Machine Learning, pages 105--142, 1999.


Boosting in the limit: Maximizing the margin of learned ensembles - Adam Grove And (1998)   (75 citations)  (Correct)

No context found.

E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: bagging, boosting, and variants. http:// robotics. stanford. edu/ users/ ronnyk, 1997.


A Constructive Algorithm for Training Cooperative Neural.. - Islam, Yao, Murase (2003)   (2 citations)  (Correct)

No context found.

E. Bauer and R. Kohavi, "An empirical comparison of voting classification algorithms: Bagging, boosting, and variants," Machine Learning, vol. 36, pp. 105--139, 1999.


Recognition Of Famous Pianists Using Machine Learning - Algorithms First Experimental (2003)   (Correct)

No context found.

Bauer, E. and Kohavi, R. (1999). An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants. Machine Learning 36:105--169.


Multi-Strategy Ensemble Learning: Reducing Error by Combining.. - Webb, Zheng (2004)   (1 citation)  (Correct)

No context found.

E. Bauer and R. Kohavi. An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36:105-- 139, 1999.

First 50 documents  Next 50

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC