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An Introduction to Machine Learning pp 43–64Cite as

Similarities: Nearest-Neighbor Classifiers

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Abstract

Two plants that look very much alike probably represent the same species; likewise, it is quite common that patients complaining of similar symptoms suffer from the same disease. In short, similar objects often belong to the same class—an observation that forms the basis of a popular approach to classification: when asked to determine the class of object x, find the training example most similar to it. Then label x with this example’s class.

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Notes

  1. 1.

    One benefit of these differences being squared, and thus guaranteed to be positive, is that this prevents negative differences, x i − y i < 0, to be subtracted from positive differences, x i − y i > 0.

  2. 2.

    Among these, perhaps the best-known are the polar distance, the Minkowski metric, and the Mahalanobis distance.

  3. 3.

    The optimal value of k (the one with the minimum error rate) is usually established experimentally.

  4. 4.

    It is fair to mention that he used them for somewhat different purposes.

  5. 5.

    www.ics.uci.edu/~mlearn/MLRepository.html.

References

  1. Cover, T. M. (1968). Estimation by the nearest neighbor rule. IEEE Transactions on Information Theory, IT-14, 50–55.

    Article  MATH  Google Scholar 

  2. Cover, T. M. & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, IT-13, 21–27.

    Article  MATH  Google Scholar 

  3. Dasarathy, B. V. (1991). Nearest-neighbor classification techniques. Los Alomitos: IEEE Computer Society Press.

    Google Scholar 

  4. Dudani, S. A. (1975). The distance-weighted k-nearest-neighbor rule. IEEE Transactions on Systems, Man, and Cybernetics, SMC-6, 325–327.

    Article  Google Scholar 

  5. Fix, E. & Hodges, J. L. (1951). Discriminatory analysis, non-parametric discrimination. USAF School of Aviation Medicine, Randolph Field, TX, Project 21-49-004, Report 4, Contract AF41(128)-3

    Google Scholar 

  6. Hart, P. E. (1968). The condensed nearest neighbor rule. IEEE Transactions on Information Theory, IT-14, 515–516.

    Article  Google Scholar 

  7. Tomek, I. (1976). Two modifications of CNN. IEEE Transactions on Systems, Man and Communications, SMC-6, 769–772.

    Article  MathSciNet  MATH  Google Scholar 

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA

    Miroslav Kubat

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  1. Miroslav Kubat
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Kubat, M. (2017). Similarities: Nearest-Neighbor Classifiers. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_3

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  • DOI: https://doi.org/10.1007/978-3-319-63913-0_3

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  • Publisher Name: Springer, Cham

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