Support vector machines(SVMs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns used for classification and regression analysis.
Advantages:
- Effective in high dimensional space
- Still effective in cases where no. of dimensional is greater than no. of samples.
- Uses a subset of training points in the decision function. So it is also memory efficient.
- Versatile: different kernal functions can be specified for the decision function. Common kernals are provided. But it also possible to specify custom kernals.
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