title: Implementing projection pursuit learning.
abstract: This paper examines the implementation of projection pursuit regression (PPR) in the context of machine learning and neural networks. We propose a parametric PPR with direct training which achieves improved training speed and accuracy when compared with nonparametric PPR. Analysis and simulations are done for heuristics to choose good initial projection directions. A comparison of a projection pursuit learning network with a single hidden-layer sigmoidal neural network shows why grouping hidden units in a projection pursuit learning network is useful. Learning robot arm inverse dynamics is used as an example problem.
authors.affiliation: Artificial Intelligence Lab., MIT, Cambridge, MA.
authors.forename: Y
authors.initials: Y
authors.lastname: Zhao
authors.forename: C G
authors.initials: CG
authors.lastname: Atkeson