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2017 | OriginalPaper | Chapter

4. Inter-Class Boundaries: Linear and Polynomial Classifiers

Author: Miroslav Kubat

Publisher: Springer International Publishing

Published in: An Introduction to Machine Learning

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Abstract

When representing the training examples with points in an n-dimensional instance space, we may realize that positive examples tend to be clustered in regions different from those occupied by negative examples. This observation motivates yet another approach to classification.

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Its author, M. Rosenblatt, originally employed this learning technique in a device he called a Perceptron.

Similarly as in the case of perceptron learning, we could have considered the 0-th attribute, x ₀ = 1, whose initial weight is w ₀ = 1.

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Title

Inter-Class Boundaries: Linear and Polynomial 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_4

Author:

Miroslav Kubat

Publisher

Springer International Publishing

Sequence number

Chapter number

Chapter 4