k-Nearest Neighbors

Sven A. Wegner ()

Chapter Chapter 3 in Mathematical Introduction to Data Science, 2024, pp 35-50 from Springer

Abstract: Abstract Given a metric space and a labeled dataset within it, we discuss several algorithms based on the concept of k-nearest neighbors. These include the k-NN classifier with majority vote and the k-NN regressor with arithmetic mean. The effect of overfitting is illustrated via several examples. We introduce some preprocessing methods and then generalize the initially mentioned setting of metric spaces to distance measures in order to include cosine similarity and cosine distance into our theory. As examples, we discuss text mining, product reviews, and handwriting recognition.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-662-69426-8_3

Ordering information: This item can be ordered from
http://www.springer.com/9783662694268

DOI: 10.1007/978-3-662-69426-8_3

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().