Add to ORKGThis paper introduces a new method using dyadic decision trees for estimating a classification or a regression function in a multi-class classification problem. The estimator is based on model selection by penalized empirical loss minimization. Our work consists in two complementary parts: first, a theoretical analysis of the method leads to deriving oracle-type inequalities for three different possible loss functions. Secondly, we present an algorithm able to compute the estimator in an exact way
International audienceA multiclass learning method which minimizes a loss function is proposed. The ...
Decision trees are well-known and established models for classification and regression. In this pape...
We consider the problem of constructing decision trees for entity identification from a given relati...
A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, an...
We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines gua...
Many machine learning applications require classifiers that minimize an asymmetric loss function rat...
Many machine learning applications require classifiers that minimize an asymmetric loss function ra...
We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs)...
We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines gua...
This paper reports on a family of computationally practical classifiers that converge to the Bayes e...
This paper reports on a family of computationally practical classifiers that converge to the Bayes e...
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
The decision tree is a well-known methodology for classification and regression. In this dissertatio...
An algorithm for learning decision trees for classification and prediction is described which conver...
A two-step procedure for nonparametric rnulticlass classifier design is described. A multiclass recu...
International audienceA multiclass learning method which minimizes a loss function is proposed. The ...
Decision trees are well-known and established models for classification and regression. In this pape...
We consider the problem of constructing decision trees for entity identification from a given relati...
A new algorithm for constructing optimal dyadic decision trees was recently introduced, analyzed, an...
We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines gua...
Many machine learning applications require classifiers that minimize an asymmetric loss function rat...
Many machine learning applications require classifiers that minimize an asymmetric loss function ra...
We propose a new nonparametric learning method based on multivariate dyadic regression trees (MDRTs)...
We introduce a new algorithm building an optimal dyadic decision tree (ODT). The method combines gua...
This paper reports on a family of computationally practical classifiers that converge to the Bayes e...
This paper reports on a family of computationally practical classifiers that converge to the Bayes e...
We improve the analysis of the decision tree boosting algorithm proposed by Mansour and McAllester. ...
The decision tree is a well-known methodology for classification and regression. In this dissertatio...
An algorithm for learning decision trees for classification and prediction is described which conver...
A two-step procedure for nonparametric rnulticlass classifier design is described. A multiclass recu...
International audienceA multiclass learning method which minimizes a loss function is proposed. The ...
Decision trees are well-known and established models for classification and regression. In this pape...
We consider the problem of constructing decision trees for entity identification from a given relati...