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Support Vector Machine (SVM) 

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.

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