Support Vector Machine

Support vector machine based on maximum interval segmentation data

The pros and cons of support vector machines:

• Advantages: The error rate is low, the calculation cost is small, the result is easy to explain.
• Cons: Sensitive to parameter adjustment and kernel function selection, the original classifier is only suitable for handling two types of problems.
• Use data types: numeric and nominal data.

The support vector is the closest point to the split plane. The next step is to try to maximize the distance from the support vector to the divider, and you need to find the optimal solution for this problem.

General framework for SVM applications

1	Collect Data	can be applied to any method
2	Preparing data	Requires numeric data
3	Analyze data	Helps visualize the separation of the superelevation plane
4	Training algorithms	Most of the time in SVM stems from training, and the country says it mainly implements tuning of two parameters
5	Test algorithm	A very simple calculation process can be achieved
6	Applicable algorithms	Almost all classification problems can use SVM, SVM itself makes a class two classifier, the application of SVM to multi-tired problem need to make some changes to the code

Support Vector Machine