• An SVM maps training examples to points in space so as to maximize the width of the gap between the two categories. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall. -Wikipedia

Is a:

• method

Applications:

• Anomaly Detection
• Classification
• Image Classification

Advantages:

• can handle non-linear classification using kernel trick, mapping inputs into high-dimensional feature spaces
• one of the most robust prediction methods -Wikipedia

Disadvantages:

• requires full labeling of input data