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AdaBoost
Regularizing Adaboost with Validation Sets of Increasing Size
Adaboost Artificial Neural Network for Stock Market Predicting
A Gentle Introduction to Gradient Boosting
The Evolution of Boosting Algorithms from Machine Learning to Statistical Modelling∗
Explaining the Success of Adaboost and Random Forests As Interpolating Classifiers
Survey of Metaheuristics and Statistical Methods for Multifactorial Diseases Analyses
A Comparison of Adaboost Algorithms for Time Series Forecast Combination
Effectiveness of Boosting Algorithms in Forest Fire Classification
Performance Analysis of Boosting Classifiers in Recognizing Activities of Daily Living
A Comparative Performance Assessment of Ensemble Learning for Credit Scoring
Adaboost Is Consistent
Explaining Adaboost
A Short Introduction to Boosting
Gradient Boosting to Build Additive Tree Models, for Example, for Representing the Logits in Logistic Regression
Component-Wise Adaboost Algorithms for High-Dimensional Binary Classification and Class Probability Prediction
The Boosting Approach to Machine Learning an Overview
Reducing the Overfitting of Adaboost by Controlling Its Data Distribution Skewness ∗ 1. Introduction the Adaptive Boosting
Soft Margins for Adaboost
Top View
Boosting Neural Networks
Boosting Algorithms: Regularization, Prediction and Model Fitting
Regularizing Adaboost
Unifying Multi-Class Adaboost Algorithms with Binary Base Learners Under the Margin Framework
Boosting Neural Networks
A Short Introduction to Boosting