Publication

DynaBoost: Combining Boosted Hypotheses in a Dynamic Way

1999
Reports

Abstract

We present an extension of Freund and Schapire's AdaBoost algorithm that allows an input-dependent combination of the base hypotheses. A separate weak learner is used for determining the input-dependent weights of each hypothesis. The error function minimized by these additional weak learners is a margin cost function that has also been shown to be minimized by AdaBoost. The weak learners used for dynamically combining the base hypotheses are simple perceptrons. We compare our dynamic combination model with AdaBoost on a range of binary and multi-class classification problems. It is shown that the dynamic approach significantly improves the results on most data sets when (rather weak) perceptron base hypotheses are used, while the difference in performance is small when the base hypotheses are MLPs.

Official source

https://infoscience.epfl.ch/entities/publication/8301949f-7326-45ba-a7ef-6b829c6309a7

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1999
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/8301949f-7326-45ba-a7ef-6b829c6309a7

About this result

Related concepts (32)

AdaBoost

AdaBoost, short for Adaptive Boosting, is a statistical classification meta-algorithm formulated by Yoav Freund and Robert Schapire in 1995, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms ('weak learners') is combined into a weighted sum that represents the final output of the boosted classifier. Usually, AdaBoost is presented for binary classification, although it can be generalized to multiple classes or bounded intervals on the real line.

Boosting (machine learning)

In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. Boosting is based on the question posed by Kearns and Valiant (1988, 1989): "Can a set of weak learners create a single strong learner?" A weak learner is defined to be a classifier that is only slightly correlated with the true classification (it can label examples better than random guessing).

Gradient boosting

Gradient boosting is a machine learning technique used in regression and classification tasks, among others. It gives a prediction model in the form of an ensemble of weak prediction models, i.e., models that make very few assumptions about the data, which are typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms random forest.

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DynaBoost: Combining Boosted Hypotheses in a Dynamic Way

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