Abstract
If a simple and fast solution for one-class classification is required, the most common approach is to assume a Gaussian distribution for the patterns of the single class. Bayesian classification then leads to a simple template matching. In this paper we show for two very different applications that the classification performance can be improved significantly if a more uniform subgaussian instead of a Gaussian class distribution is assumed. One application is face detection, the other is the detection of transcription factor binding sites on a genome. As for the Gaussian, the distance from a template, i.e., the distribution center, determines a pattern’s class assignment. However, depending on the distribution assumed, maximum likelihood learning leads to different templates from the training data. These new templates lead to significant improvements of the classification performance.
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Madany Mamlouk, A., Kim, J.T., Barth, E., Brauckmann, M., Martinetz, T. (2003). One-Class Classification with Subgaussians. In: Michaelis, B., Krell, G. (eds) Pattern Recognition. DAGM 2003. Lecture Notes in Computer Science, vol 2781. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45243-0_45
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DOI: https://doi.org/10.1007/978-3-540-45243-0_45
Publisher Name: Springer, Berlin, Heidelberg
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