Abstract
More recently, neural network techniques and fuzzy logic inference systems have been receiving an increasing attention. At the same time, methods of establishing decision by a group of classifiers are regarded as a general problem in various application areas of pattern recognition. Similarly to standard two-class pattern recognition methods, one-class classifiers hardly ever fit the data distribution perfectly. The paper presents fuzzy models for one-class classifier combination, compares their computational and expected space complexity with the one from ECOC and decision templates, presents possibility to speed up a fuser processing by means of multithreading.
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Wilk, T., Woźniak, M. (2011). Complexity and Multithreaded Implementation Analysis of One Class-Classifiers Fuzzy Combiner. In: Corchado, E., Kurzyński, M., Woźniak, M. (eds) Hybrid Artificial Intelligent Systems. HAIS 2011. Lecture Notes in Computer Science(), vol 6679. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21222-2_29
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DOI: https://doi.org/10.1007/978-3-642-21222-2_29
Publisher Name: Springer, Berlin, Heidelberg
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