Add to ORKGWe give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logistic Regression and the Least-Square-Boost algorithm for regression. These methods have in common that they iteratively call a base learning algorithm which returns hypotheses that are then linearly combined. We show that these methods are related to the Gauss-Southwell method known from numerical optimization and state non-asymptotical convergence results for all these methods. Our analysis includes ¤¦ ¥-norm regularized cost functions leading to a clean and general way to regularize ensemble learning
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
Boosting is a learning scheme that combines weak learners to produce a strong composite learner, wit...
A key open problem in reinforcement learning is to assure convergence when using a compact hy-pothes...
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logist...
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logist...
Abstract. We give a unified account of boosting and logistic regression in which each learning probl...
International audienceIn this paper, we investigate the impact of compression on stochastic gradient...
A regularized boosting method is introduced, for which regularization is obtained through a penaliza...
An asymptotic theory for estimation and inference in adaptive learning models with strong mixing reg...
We investigate machine learning for the least square regression with data dependent hypothesis and c...
Ensemble machine learning methods are often used when the true prediction function is not easily app...
LogitBoost, MART and their variant can be viewed as additive tree regression using logis-tic loss an...
Boosting combines weak learners into a predictor with low empirical risk. Its dual constructs a high...
Ensemble learning by variational free energy minimization is a tool introduced to neural networks by...
We present a new ensemble learning algorithm, DeepBoost, which can use as base classifiers a hypothe...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
Boosting is a learning scheme that combines weak learners to produce a strong composite learner, wit...
A key open problem in reinforcement learning is to assure convergence when using a compact hy-pothes...
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logist...
We give an unified convergence analysis of ensemble learning methods including e.g. AdaBoost, Logist...
Abstract. We give a unified account of boosting and logistic regression in which each learning probl...
International audienceIn this paper, we investigate the impact of compression on stochastic gradient...
A regularized boosting method is introduced, for which regularization is obtained through a penaliza...
An asymptotic theory for estimation and inference in adaptive learning models with strong mixing reg...
We investigate machine learning for the least square regression with data dependent hypothesis and c...
Ensemble machine learning methods are often used when the true prediction function is not easily app...
LogitBoost, MART and their variant can be viewed as additive tree regression using logis-tic loss an...
Boosting combines weak learners into a predictor with low empirical risk. Its dual constructs a high...
Ensemble learning by variational free energy minimization is a tool introduced to neural networks by...
We present a new ensemble learning algorithm, DeepBoost, which can use as base classifiers a hypothe...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
Boosting is a learning scheme that combines weak learners to produce a strong composite learner, wit...
A key open problem in reinforcement learning is to assure convergence when using a compact hy-pothes...