Abstract
This chapter will give a brief general introduction into the field of classification and an overview of some important types of classifiers. Classification is a research area in its own right, which over the last 40 years has combined results from disciplines as different as biology, psychology, mathematics, and computer science. Covering such an extensive and varied scientific field with even a minimal claim to completeness is naturally beyond the scope of this text, as is treating all commonly used types of classifiers in mathematical detail. Yet we will at least mention the principal types to give the reader some orientation in this important area of pattern recognition tasks. We will therefore discuss the multilayer perceptron neural network used in the pattern recognition examples of Chap. 5 in depth.
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Notes
- 1.
For analog values it would actually be correct to speak of probability densities, but the basic argumentation stays the same.
- 2.
There is some disagreement in the literature about the nomenclature; some authors call this a three-layered network since there are three layers of processing units, others speak of two-layered networks, arguing that the input layer only collects the input values and performs no processing itself.
- 3.
Again various notational conventions are used in the literature; we will always name the unit from which the connection goes out first, the receiving unit last.
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Demant, C., Garnica, C., Streicher-Abel, B. (2013). Overview: Classification. In: Industrial Image Processing. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33905-9_6
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DOI: https://doi.org/10.1007/978-3-642-33905-9_6
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