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2017 | OriginalPaper | Chapter

14. Unsupervised Learning

Author: Miroslav Kubat

Publisher: Springer International Publishing

Published in: An Introduction to Machine Learning

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Abstract

It would be a mistake to think that machine learning always requires examples with class labels. Far from it! Useful information can be gleaned even from examples whose classes are not known. This is sometimes called unsupervised learning, in contrast to the term supervised learning which is used when talking about induction from pre-classified examples.

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Machine learning professionals sometimes avoid the term “center” which might imply mathematical properties that are for the specific needs of cluster analysis largely irrelevant.

For instance, the section on RBF networks denoted these centers by μ _i’s.

In statistics, and in neural networks, scientists often use the term feature instead of attribute.

Recall that one epoch means that all training examples have been presented once.

A bulldozer is more powerful than a spade, and yet the gardener prefers the spade most of the time.

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Title

Unsupervised Learning

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_14

Author:

Miroslav Kubat

Publisher

Springer International Publishing

Sequence number

Chapter number

Chapter 14