Support Vector Machine and Kernel Classification Algorithms

This chapter introduces the basics of support vector machine (SVM) and other kernel classifiers for pattern recognition and detection. It also introduces the main elements and concept underlying the successful binary SVM. The chapter starts by introducing the main elements and concept underlying the...

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Veröffentlicht in:Digital Signal Processing with Kernel Methods S. 433 - 502
Hauptverfasser: Rojo-Álvarez, José Luis, Martínez-Ramón, Manel, Muñoz-Mar&iacute, Jordi, Camps-Valls, Gustau
Format: Buchkapitel
Sprache:Englisch
Veröffentlicht: Chichester, UK Wiley 2018
John Wiley & Sons, Ltd
Ausgabe:1
Schriftenreihe:Wiley - IEEE
Schlagworte:
active learning
brain–computer interface signal classification
digital signal processing
kernel classification algorithms
large margin filtering
linear algorithm
SSL
support vector machine
Support vector machines
Training
Signal processing algorithms
Fasteners
Cost function
Classification algorithms
Kernel
ISBN:9781118611791, 1118611799
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:This chapter introduces the basics of support vector machine (SVM) and other kernel classifiers for pattern recognition and detection. It also introduces the main elements and concept underlying the successful binary SVM. The chapter starts by introducing the main elements and concept underlying the successful binary SVM. Next, it introduces more advanced topics in SVM for classification, including large margin filtering (LMF), SSL, active learning, and large‐scale classification using SVMs. The LMF method performs both signal filtering and classification simultaneously by learning the most appropriate filters. SSL with SVMs exploits the information contained in both labeled and unlabeled examples. The chapter further illustrates the capabilities of the parallel SVM (PSVM) for classification of large‐scale real data problems. PSVM performs parallel IPM to solve the QP optimization problem, while the computation and the memory demands are improved with respect to other decomposition‐based algorithms.
ISBN:9781118611791
1118611799
DOI:10.1002/9781118705810.ch10