Abstract
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.
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Fujino, T., Ishiguro, K., Sawada, H. (2011). Logitboost Extension for Early Classification of Sequences. In: Real, P., Diaz-Pernil, D., Molina-Abril, H., Berciano, A., Kropatsch, W. (eds) Computer Analysis of Images and Patterns. CAIP 2011. Lecture Notes in Computer Science, vol 6854. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23672-3_70
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DOI: https://doi.org/10.1007/978-3-642-23672-3_70
Publisher Name: Springer, Berlin, Heidelberg
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