A Theory of Probabilistic Boosting, Decision Trees and Matryoshki
| dc.creator | Grossmann, Etienne | |
| dc.date | 2006-07-25 | |
| dc.date.accessioned | 2026-07-07T07:16:23Z | |
| dc.date.available | 2026-07-07T07:16:23Z | |
| dc.description | 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 probabilistic weak learner/classifier and compare three types of boosting algorithms: probabilistic Adaboost, decision tree, and tree of trees of ... of trees, which we call matryoshka. "Nested tree," "embedded tree" and "recursive tree" are also appropriate names for this algorithm, which is one of our contributions. Our other contribution is the theoretical analysis of the algorithms, in which we give training error bounds. This analysis suggests that the matryoshka leverages probabilistic weak classifiers more efficiently than simple decision trees. | |
| dc.identifier | https://arxiv.org/abs/cs/0607110 | |
| dc.identifier | http://arxiv.org/abs/cs/0607110 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/113574 | |
| dc.subject | Machine Learning | |
| dc.subject | I.5.1; I.2.6; G.3 | |
| dc.title | A Theory of Probabilistic Boosting, Decision Trees and Matryoshki | |
| dc.type | text |