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2017 | OriginalPaper | Chapter

3. Similarities: Nearest-Neighbor Classifiers

Author: Miroslav Kubat

Publisher: Springer International Publishing

Published in: An Introduction to Machine Learning

» Get access to the full version

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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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.

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

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

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

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Title

Similarities: Nearest-Neighbor Classifiers

Book

An Introduction to Machine Learning

Print ISBN: 978-3-319-63912-3

Electronic ISBN: 978-3-319-63913-0

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

DOI

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

Author:

Miroslav Kubat

Publisher

Springer International Publishing

Sequence number

Chapter number

Chapter 3