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6. Support Vector Machine

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Karol Przystalski
Maciej J. Ogorzałek
Jan K. Argasiński
Wiesław Chmielnicki
Published in
Pattern Recognition Primer
Publisher
Springer Nature Switzerland

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Abstract

This chapter delves into the Support Vector Machine (SVM), a robust classifier introduced by Vapnik, focusing on its core principles and practical applications. It begins by explaining the concept of the optimal separating hyperplane, which efficiently distinguishes elements of two different classes. The text then explores the soft margin approach to handle noisy samples and the use of kernel functions to tackle non-linearly separable problems. Additionally, it covers extensions of SVM for one-class problems and multiclass classification strategies, such as one-vs-one and one-vs-rest methods. The chapter also provides Python code snippets for implementing SVM with different kernels, demonstrating its practical application. By the end, readers will gain a comprehensive understanding of SVM's theoretical foundations and its implementation in real-world scenarios.
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previous chapter Decision Trees
next chapter Ensemble Methods
Title
Support Vector Machine
Authors
Karol Przystalski
Maciej J. Ogorzałek
Jan K. Argasiński
Wiesław Chmielnicki
Copyright Year
2026
Book
Pattern Recognition Primer

Print ISBN: 978-3-031-91815-5

Electronic ISBN: 978-3-031-91816-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-031-91816-2_6
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