A general framework for function approximation from finite data is presented based on reproducing kernel Hilbert spaces. Key results are summarised and the normal and regularised solutions are described. A potential limitation to these solutions for large data sets is the computational burden. An iterative approach to the least-squares normal solution is proposed to overcome this. Detailed proofs of convergence are given.