@inbook {IOPORT.05282573,
    author       = {Vincent, Robert D. and Pineau, Joelle and de Guzman, Philip and Avoli, Massimo},
    title        = {Recurrent boosting for classification of natural and synthetic time-series data.},
    year         = {2007},
    booktitle    = {Advances in artificial intelligence. 20th conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007, Montreal, Canada, May 28--30, 2007. Proceedings},
    isbn         = {978-3-540-72664-7},
    pages        = {192-203},
    publisher    = {Berlin: Springer},
    doi          = {10.1007/978-3-540-72665-4_17},
    abstract     = {Summary: Boosted ensemble classifiers have a demonstrated ability to discover regularities in large, poorly modeled datasets. In this paper we present an application of multi-hypothesis AdaBoost to detect epileptiform activity from electrophysiological recordings. While existing boosting methods do not account automatically for the sequence information that is available when analyzing time-series data, we present a recurrent extension to AdaBoost, and show that it improves classification accuracy in our application domain.},
    identifier   = {05282573},
}