We propose a new boosting method for classification of time sequences. In the problem of on-line classification, it is essential to classify time sequences as quickly as possible in many practical cases. This type of classification is called "early classification." Recently, an Adaboost-based "Earlyboost" has been proposed, which is known for its efficiency. In this paper, we propose a Logitboost-based early classification for further improvements of Earlyboost. We demonstrate the structure of the proposed method, and experimentally verify its performance. © 2011 Springer-Verlag.
CITATION STYLE
Fujino, T., Ishiguro, K., & Sawada, H. (2011). Logitboost extension for early classification of sequences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6854 LNCS, pp. 579–588). https://doi.org/10.1007/978-3-642-23672-3_70
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