| ### Chapter 12 - Constructive Methods
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Russell D. Reed and Robert J. Marks II
Copyright © 1999 Massachusetts Institute of Technology

## 12.9 Other Algorithms

The preceding sections list only a few of the many algorithms that have been proposed. The list is not exhaustive by any means so we encourage the reader to explore further for a more complete survey. Genetic algorithms, for example, have been proposed to both generate the network structure and find the appropriate weights. Some of the weight initialization techniques mentioned in chapter 7 construct networks based on solutions provided by other method, for example, decision trees (section 7.2.5) or rule-based knowledge (section 7.2.6). A polynomial time algorithm using clustering and linear programming techniques to generate classifier networks is described in [276]. Projection pursuit regression [129], [185], a well-known statistical procedure, creates a system similar to a single-hidden-layer network with a linear output node. It is constructive in the sense that it adds projection directions (corresponding to hidden units) sequentially until the error is sufficiently small.