Abstract
Support vector machines (SVMs) are a set of related methods for supervised learning, applicable to both classification and regression problems. An SVM classifier creates a maximum-margin hyperplane that lies in a transformed input space and splits the example classes while maximizing the distance to the nearest cleanly split examples. The parameters of the solution hyperplane are derived from a quadratic programming optimization problem. In this chapter, we provide several formulations and discuss some key concepts.
Original language | English |
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Title of host publication | Machine Learning for Data Science Handbook |
Subtitle of host publication | Data Mining and Knowledge Discovery Handbook, Third Edition |
Publisher | Springer International Publishing |
Pages | 93-110 |
Number of pages | 18 |
ISBN (Electronic) | 9783031246289 |
ISBN (Print) | 9783031246272 |
DOIs | |
State | Published - 1 Jan 2023 |
ASJC Scopus subject areas
- General Computer Science
- General Mathematics