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Support Vector Machine (SVM)

Definition - What does Support Vector Machine (SVM) mean?

A support vector machine (SVM) is machine learning algorithm that analyzes data for classification and regression analysis. SVM is a supervised learning method that looks at data and sorts it into one of two categories. An SVM outputs a map of the sorted data with the margins between the two as far apart as possible. SVMs are used in text categorization, image classification, handwriting recognition and in the sciences.

A support vector machine is also known as a support vector network (SVN).

Techopedia explains Support Vector Machine (SVM)

A support vector machine is a supervised learning algorithm that sorts data into two categories. It is trained with a series of data already classified into two categories, building the model as it is initially trained. The task of an SVM algorithm is to determine which category a new data point belongs in. This makes SVM a kind of non-binary linear classifier.

An SVM algorithm should not only place objects into categories, but have the margins between them on a graph as wide as possible.

Some applications of SVM include:

  • Text and hypertext classification
  • Image classification
  • Recognizing handwritten characters
  • Biological sciences, including protein classification

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