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What is SVM Optimization and Cost Functions about?

1. Support Vector Machines (SVMs) are an alternative to logistic regression that find a decision boundary with the largest minimum distance to the nearest data points of any class. 2. SVMs can learn non-linear decision boundaries using kernel methods, which implicitly map inputs to high-dimensional feature spaces. 3. When using an SVM, users must select parameters like the cost parameter C and the kernel function, and software packages can then solve for the optimal parameters.

Author

Lôny Nêz

Language

EN