title: Constructive algorithms for structure learning in feedforward neural networks for regression problems.
abstract: In this survey paper, we review the constructive algorithms for structure learning in feedforward neural networks for regression problems. The basic idea is to start with a small network, then add hidden units and weights incrementally until a satisfactory solution is found. By formulating the whole problem as a state-space search, we first describe the general issues in constructive algorithms, with special emphasis on the search strategy. A taxonomy, based on the differences in the state transition mapping, the training algorithm, and the network architecture, is then presented.
authors.affiliation: Dept. of Comput. Sci., Hong Kong Univ. of Sci. and Technol., Kowloon.
authors.forename: T Y
authors.initials: TY
authors.lastname: Kwok
authors.forename: D Y
authors.initials: DY
authors.lastname: Yeung