In Machine learning, support vector machines (SVM) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for Classification, Regression analysis and Outliers detection. (http://en.wikipedia.org/wiki/Support_vector_machine)

장점

• high dimensional space에 효과적
• dimension의 수가 sample 보다 많을 때 효과적
• decision function에서 학습 지점 서브셋을 쓰며, 메모리 효율.
• 다재다능함

단점

• feature의 수가 sample 보다 많을 때는 kernel function을 선택하면서 overfitting을 줄여야 함
• 직접 probability 예측을 하지 않음

Classification

• SVC (C-Support Vector Classification)
• NuSVC (Nu-Support Vector Classification)
• LinearSVC (Linear Support Vector Classification)

Regression

• SVR
• NuSVR
• LinearSVR

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