Keywords: SVM, RVM, Sparse Kernel technique, Python
This is the Chapter7 ReadingNotes from book Bishop-Pattern-Recognition-and-Machine-Learning-2006. [Code_Python]
One of the significant limitations of many such algorithms is that the kernel function $k(\pmb{x_n}, \pmb{x_m})$ must be evaluated for all possible pairs xn and xm of training points, which can be computationally infeasible during training and can lead to excessive computation times when making predictions for new data points.
In this chapter we shall look at kernel-based algorithms that have sparse solutions, so that predictions for new inputs depend only on the kernel function evaluated at a subset of the training data points.
