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Nearest Neighbour Based Classifiers

  • Chapter
Pattern Recognition

Part of the book series: Undergraduate Topics in Computer Science ((UTICS,volume 0))

Abstract

One of the simplest decision procedures that can be used for classification is the nearest neighbour (NN) rule. It classifies a sample based on the category of its nearest neighbour. When large samples are involved, it can be shown that this rule has a probability of error which is less than twice the optimum error—hence there is less than twice the probability of error compared to any other decision rule. The nearest neighbour based classifiers use some or all the patterns available in the training set to classify a test pattern. These classifiers essentially involve finding the similarity between the test pattern and every pattern in the training set.

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Authors and Affiliations

  1. Dept. of Computer Science and Automation, Indian Institute of Science, Bangalore, India

    Prof. M. Narasimha Murty & Dr. V. Susheela Devi

Authors
  1. Prof. M. Narasimha Murty
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  2. Dr. V. Susheela Devi
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Correspondence to M. Narasimha Murty .

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© 2011 Universities Press (India) Pvt. Ltd.

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Murty, M.N., Devi, V.S. (2011). Nearest Neighbour Based Classifiers. In: Pattern Recognition. Undergraduate Topics in Computer Science, vol 0. Springer, London. https://doi.org/10.1007/978-0-85729-495-1_3

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  • DOI: https://doi.org/10.1007/978-0-85729-495-1_3

  • Publisher Name: Springer, London

  • Print ISBN: 978-0-85729-494-4

  • Online ISBN: 978-0-85729-495-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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