Machine Learning Algorithms

Kernel-based classification

When working with non-linear problems, it's useful to transform the original vectors by projecting them into a higher dimensional space where they can be linearly separated. We saw a similar approach when we discussed polynomial regression. SVMs also adopt the same approach, even if there's now a complexity problem that we need to overcome. Our mathematical formulation becomes:

Every feature vector is now filtered by a non-linear function that can completely reshape the scenario. However, the introduction of such a function increased the computational complexity in a way that could apparently discourage this approach. ...