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PATTERN RECOGNITION

Pattern Recognition, 2018, p.53-99

2018 John Wiley & Sons, Inc. ;ISBN: 111930282X ;ISBN: 9781119302827 ;EISBN: 1119302870 ;EISBN: 9781119302872 ;DOI: 10.1002/9781119302872.ch2

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  • Title:
    PATTERN RECOGNITION
  • Author: Homenda, Władysław ; Pedrycz, Witold
  • Subjects: classifiers ; decision trees ; ensemble classifiers ; feature space geometry ; mapping ; nearest neighbors classification method ; pattern recognition ; support vector machines classification algorithm
  • Is Part Of: Pattern Recognition, 2018, p.53-99
  • Description: 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.
  • Publisher: Hoboken, NJ, USA: John Wiley & Sons, Inc
  • Language: English
  • Identifier: ISBN: 111930282X
    ISBN: 9781119302827
    EISBN: 1119302870
    EISBN: 9781119302872
    DOI: 10.1002/9781119302872.ch2
  • Source: Ebook Central Academic Complete
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