Abstract
This chapter gives an introduction to pattern recognition and machine learning via K-nearest neighbors. Nearest neighbor methods will have an important part to play in this book. The chapter starts with an introduction to foundations in machine learning and decision theory with a focus on classification and regression. For the model selection problem, basic methods like cross-validation are introduced. Nearest neighbor methods are based on the labels of the K-nearest patterns in data space. As local methods, nearest neighbor techniques are known to be strong in case of large data sets and low dimensions. Variants for multi-label classification, regression, and semi supervised learning settings allow the application to a broad spectrum of machine learning problems. Decision theory gives valuable insights into the characteristics of nearest neighbor learning results.
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© 2013 Springer-Verlag Berlin Heidelberg
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Kramer, O. (2013). K-Nearest Neighbors. In: Dimensionality Reduction with Unsupervised Nearest Neighbors. Intelligent Systems Reference Library, vol 51. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38652-7_2
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DOI: https://doi.org/10.1007/978-3-642-38652-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38651-0
Online ISBN: 978-3-642-38652-7
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