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- W4248387443 abstract "This chapter presents selected classifiers: fc-nearest neighbors (fc-NN), support vector machines (SVMs), decision trees, ensemble classifiers, and a particular example of ensemble classifiers-random forests and naive Bayes classifier. It proceeds with a detailed discussion and presents selected algorithms that operate in the feature space and facilitate classification mechanisms. It discusses architectures and design approaches to fundamental classifiers such as selected probabilistic classifiers, classifiers based on feature space geometry, and ensemble classifiers. Constructing a classifier T with the aid of the learning set is an ultimate objective of pattern recognition. Pattern recognition is usually performed on observed features characterizing patterns rather than on the patterns directly. Therefore, one can distinguish between a mapping from the space of patterns into the space features. The chapter then explores pattern recognition problems searching for a classifier, that is, a mapping." @default.
- W4248387443 created "2022-05-12" @default.
- W4248387443 date "2018-02-09" @default.
- W4248387443 modified "2023-10-14" @default.
- W4248387443 title "PATTERN RECOGNITION" @default.
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- W4248387443 doi "https://doi.org/10.1002/9781119302872.ch2" @default.
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