## Performance considerations

You may have already observed that the training of a model for the support vector regression on a large dataset is time consuming. The performance of the support vector machine depends on the type of optimizer (for example, a sequential minimal optimization) selected to maximize the margin during training:

A linear model (a SVM without kernel) has an asymptotic time complexity

*O(N)*for training*N*labeled observations.Nonlinear models rely on kernel methods formulated as a quadratic programming problem with an asymptotic time complexity of

*O(N*^{3})An algorithm that uses sequential minimal optimization techniques, such as index caching or elimination of null values (as in LIBSVM), has an asymptotic time complexity of

*O(N*with the worst case scenario (quadratic optimization) of^{2})*O(N*^{3})Sparse problems for very large training sets (

*N > 10,000*) also have an asymptotic time of*O(N*^{2})

The time and space complexity of the kernelized support vector machine has been receiving...