Jul 25, 2006 · This analysis suggests that the matryoshka leverages probabilistic weak classifiers more efficiently than simple decision trees. Subjects: ...

We have developed in this paper a theory of probabilistic boosting, aimed at decision trees. We proposed a boosting tree algorithm and a theoretically ...

This analysis suggests that the matryoshka leverages probabilistic weak classifiers more efficiently than simple decision trees. We present a theory of ...

We present a theory of boosting probabilistic classifiers. We place ourselves in the situation of a user who only provides a stopping parameter and a ...

TL;DR: This analysis suggests that the matryoshka leverages probabilistic weak classifiers more efficiently than simple decision trees. read more. Abstract: We ...

A Theory of Probabilistic Boosting, Decision Trees and Matryoshki · E. Grossmann. Computer Science, Mathematics. ArXiv. 2006. TLDR. This analysis suggests that ...

A Theory of Probabilistic Boosting, Decision Trees and Matryoshki. August 2006. Etienne Grossmann. We present a theory of boosting probabilistic classifiers.

Missing: Theory Probabilistic Boosting,

Jun 3, 2021 · We propose Probabilistic Gradient Boosting Machines (PGBM), a method to create probabilistic predictions with a single ensemble of decision trees in a ...

Decision trees and ensembles of decision trees are very popular in machine learning and often achieve state-of-the-art performance on black-box prediction ...