Standard 3 stops to get here · leads to 1
Support Vector Machine
A supervised learning algorithm that finds the optimal hyperplane to separate classes with maximum margin.
Your route here
3 stops · basics first
- Machine Learning ✓ understood
Building systems that learn patterns from data instead of following hand-written rules, getting better at a task as they see more examples.
- Supervised Learning ✓ understood
Learning from examples paired with the correct answer, so a model can predict answers for new inputs it hasn't seen.
- Classification ✓ understood
A supervised learning task where the model assigns each input to one of a fixed set of categories, such as spam or not spam.
- Support Vector Machine · you are here ✓ understood
Where it sits
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Foundations Decision Tree A tree-structured model that makes decisions by splitting data based on feature values, interpretable but prone to overfitting. Foundations Gaussian Process A non-parametric Bayesian approach for regression and classification, defining distributions over functions. Training Hinge Loss A loss function for maximum-margin classification, used in SVMs. Neural Networks Radial Basis Function Network A neural network using radial basis functions as activation functions, useful for function approximation and interpolation. Foundations VC Dimension A measure of model capacity - the largest set of points a model can shatter (classify in all possible ways).
In the research
All papers →A paper that builds on Support Vector Machine .
