Abstract
The paper presents algorithms of the multitask recognition for the direct approach and for the decomposed independent approach. Both algorithms are presented in the even of full probabilistic information. Algorithms with full probabilistic information were working on basis of Bayes decision theory. Full probabilistic information in a pattern recognition task, denotes the knowledge of the classes probabilities and the class-conditional probability density functions. Optimal algorithms for the selected loss function will be presented.
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Puchala, E. (2004). A Bayes Algorithm for the Multitask Pattern Recognition Problem – Direct and Decomposed Independent Approaches. In: Laganá, A., Gavrilova, M.L., Kumar, V., Mun, Y., Tan, C.J.K., Gervasi, O. (eds) Computational Science and Its Applications – ICCSA 2004. ICCSA 2004. Lecture Notes in Computer Science, vol 3046. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24768-5_5
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DOI: https://doi.org/10.1007/978-3-540-24768-5_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22060-2
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