Year: 2012
Type: preprint
Abstract: In this paper we propose an algorithm that builds sparse decision DAGs (directed acyclic graphs) from a list of base classifiers provided by an external learning method such as AdaBoost. The basic idea is to cast the DAG design task as a Markov decision process. Each instance can decide to use or to skip each base classifier, based on the current state of the classifier being built. The result is a sparse decision DAG where the base classifiers are selected in a data-dependent way. The method has a single hyperparameter with a clear semantics of controlling the accuracy/speed trade-off. The algorithm is competitive with state-of-the-art cascade detectors on three object-detection benchmarks, and it clearly outperforms them when there is a small number of base classifiers. Unlike cascades, it is also readily applicable for multi-class classification. Using the multi-class setup, we show on a benchmark web page ranking data set that we can significantly improve the decision speed without harming the performance of the ranker.
Source: arXiv (Cornell University)
Institutions: Université Paris-Sud, Centre National de la Recherche Scientifique, Institut National de Physique Nucléaire et de Physique des Particules, Laboratoire de Recherche en Informatique, Laboratoire de l'Accélérateur Linéaire, University of Szeged, MTA-SZTE Research Group on Artificial Intelligence, Hungarian Academy of Sciences
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Agence Nationale de la Recherche
Award: G225135577
OpenAlex ID: W2147398243