Some Approximation Properties of Projection Pursuit Learning Networks
Ying Zhao, Christopher G. Atkeson · Neural Information Processing Systems · 1991
This paper will address an important question in machine learning: What kind of network architectures work better on what kind of problems? A projection pursuit learning network has a very similar structure to a one hidden layer sigmoidal neural network. A general method based on a continuous version of projection pursuit regression is developed to show that projection pursuit regression works better on angular smooth functions than on Laplacian smooth functions. There exists a ridge function approximation scheme to avoid the curse of dimensionality for approximating functions in L2(θd).