Basics - SUPPORT VECTOR MACHINES

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Key Notes

• Allow non-linear decision boundaries
• SVM - Out of box supervised learning technique
• Feature Space - Finite dimensional vector space
• Each dimension represents feature
• Goal of SVN - Train a model that assigns unseen objects into particular category
• Creates linear partition of feature space
• Based on features it places above or below separation linear
• No stochastic element involved (No involvement of any previous state status)
• support vector classifiers or soft margin classifiers - allows some observations to be on in-correct side of hyperplane allowing soft margin

Advantage

• High Dimensionality, Memory Efficiency, Versatility

Disadvantages

• Non probabilistic

More Reads

Guide to Machine Learning

Happy Learning!!!