Support Vector Machine

Christo El Morr, Manar Jammal, Hossam Ali-Hassan and Walid El-Hallak ()
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Christo El Morr: York University
Manar Jammal: York University
Hossam Ali-Hassan: York University, Glendon Campus
Walid El-Hallak: Ontario Health

Chapter Chapter 13 in Machine Learning for Practical Decision Making, 2022, pp 385-411 from Springer

Abstract: Abstract The more the dimensions of a feature space, the more is the computing power needed to classify. Support vector machines (SVMs) main advantages are (1) their effectiveness in a high-dimensional space and in cases where the number of dimensions is higher than the number of instances in the dataset, and (2) their low use of memory and hence their memory efficiency.

Date: 2022
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DOI: 10.1007/978-3-031-16990-8_13

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