Data Science, Machine Learning, Algorithms, Support Vector Machine, Supervised Learning.
the kernel trick. In the end, SVMs make two important assumptions: Data is linearly separable. Even if the linear boundary is in an augmented feature space. The model is represented using inner products, so that kernels can be used. Let’s look at a few examples To see support vector machines in