Projection Pursuit Regression Vs Neural Network. Aug 01 2015 To this end the projection pursuit regression PPR is proposed. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner.
Exactly the same form as the projection pursuit model described above. The GAIM is shown to have close connections with feedforward neural networks Hwang et al 1994. P 2 the regression surface looks like a corrugated sheet and is constant in the directions orthogonal to α k.
Fx P M m1 g mw 0x where each w m is a vector of weights and g m is a smooth.
Deep neural nets by which people mean nets with more than one hidden layer are a form of neural network. The other is the projection pursuit learning PPL. Iterative algorithms are used. PPR vs neural networks NN Both projection pursuit regression and neural networks models project the input vector onto a one-dimensional hyperplane and then applies a nonlinear transformation of the input variables that are then added in a linear fashion.
