nting Projection Pursuit Le

This paper examines the implementation of projec- tion 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 simu- lations are done for heuristics to choose good initial projection directions. A comparison of a projection pursuit learning network with a one 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.

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