R. Avimelech, N. Intrator, Boosting Regression Estimators, Neural Computation, 11 (2) (1999) Home/Search Document Details and Download
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Robust Regression by Boosting the Median - Kegl (Correct)
....domain, the idea of using the weighted median as the final regressor is not new. Freund [6] briefly mentions it and proves a special case of the main theorem of this paper. The ADABOOST.R algorithm of Freund and Schapire [7] returns the weighted median but the response space is restricted to [0, 1] and the parameter updating steps are rather complicated. Drucker [4] also uses the weighted median of the base regressors as the final regressor but the parameter updates are heuristic and the convergence of the method is not analyzed. Bertoni et al. 2] consider an algorithm similar to MEDBOOST ....
....updating steps are rather complicated. Drucker [4] also uses the weighted median of the base regressors as the final regressor but the parameter updates are heuristic and the convergence of the method is not analyzed. Bertoni et al. 2] consider an algorithm similar to MEDBOOST with response space [0, 1], and prove a convergence theorem in that special case that is weaker than our result by a factor of two. Avnimelech and Intrator [1] construct triplets of weak learners and show that the median of the three regressors has a smaller error than the individual regressors. The idea of using the ....
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R. Avnimelech and N. Intrator. Boosting regression estimators. Neural Computation, 11:491--513, 1999.
Bagging and Boosting for the Nearest Mean Classifier.. - Skurichina, Kuncheva.. (2002) (3 citations) (Correct)
....section 6. 2 Combining Techniques Bagging and boosting are ensemble design techniques that allow us to improve the performance of weak classifiers. Originally, they were designed for decision trees [5, 6] However, they were found to perform well for other classification rules: neural networks [12], linear classifiers [11] and k nearest neighbour classifiers [5] It was shown that for linear classifiers, the performance of bagging and boosting is affected by the training sample size, the choice of the base classifier and the choice of the combining rule [11] Bagging is useful for linear ....
Avnimelech, R., Intrator, N.: Boosting Regression Estimators. In: Neural Computation 11 (1999) 499-520
Active Subset Selection Approach To Nonlinear Modeling.. - Merkwirth, Wichard.. (2003) (Correct)
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R. Avimelech, N. Intrator, Boosting Regression Estimators, Neural Computation, 11 (2) (1999)
Diversity in Neural Network Ensembles - Brown (2003) (1 citation) (Correct)
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R. Avnimelech and N. Intrator. Boosting regression estimators. Neural Computation, 11:491--513, 1999.
Diversity in Neural Network Ensembles - Gavin Brown To (2003) (1 citation) (Correct)
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R. Avnimelech and N. Intrator. Boosting regression estimators. Neural Computation, 11:491--513, 1999.
Aggregation Algorithms for - Neural Network Ensemble (Correct)
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R. Avnimelech and N. lntrator, "Boosting regression estimators", Neural Computation 11, 499 (1999)
An Empirical Comparison Of two Sampling Techniques For Training.. - Lipnickas (2000) (Correct)
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R. Avnimelech, N. Intrator. Boosting regression esti- mators. Neural Computation, 11, 1999, 499-520.
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