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
Incoming Links #
Related Articles (Article 0) #
Related Codes (Code 1) #
Suggested Pages #
- 0.136 Hidden Markov model
- 0.112 PCA
- 0.071 scikit-image
- 0.062 Scikit Flow
- 0.042 Artificial neural network
- 0.040 AdaBoost
- 0.025 FMM Neural Network
- 0.025 Multiple regression
- 0.025 Nonlinear regression
- 0.025 Eugenics
- More suggestions...