What does "Support Vector Machine (SVM) " mean?

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.

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.

Where can you find this term?

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