Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression tasks. It identifies an optimal hyperplane in a high-dimensional space that separates classes with the maximum margin, thereby maximizing classification accuracy.
- Glossary > Letter: S
What does "Support Vector Machine (SVM) " mean?

Use Cases
Text and Hypertext Categorization:
Classifying documents based on content and links.
Image Classification:
Identifying objects within images by separating them into distinct categories.
Bioinformatics:
Predicting the classification of genes and protein sequences.

Importance
Effective in High-Dimensional Spaces:
Performs well in datasets with many features or dimensions.
Maximal Margin Classifier:
Identifies a decision boundary that maximizes the separation between classes.
Versatility:
Applies to both linear and non-linear classification problems through kernel tricks.

Analogies
Support Vector Machine is like drawing a line between two groups of people based on their height and weight. Just as you draw a line that maximizes the gap between the tallest person in one group and the shortest in the other, SVM finds a hyperplane that maximally separates data points in higher-dimensional spaces.
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