Shultz, T. R., & Rivest, F. (2001).  Knowledge-based cascade-correlation: Using knowledge to speed learning. Connection Science, 13, 43-72.



Research with neural networks typically ignores the role of knowledge in learning by initializing the network with random connection weights. We examine a new extension of a well-known generative algorithm, cascade-correlation. Ordinary cascade-correlation constructs its own network topology by recruiting new hidden units as needed to reduce network error. The extended algorithm, knowledge-based cascade-correlation (KBCC), recruits previously learned sub-networks as well as single hidden units. This paper describes KBCC and assesses its performance on a series of small, but clear problems involving discrimination between two classes. The target class is distributed as a simple geometric figure. Relevant source knowledge consists of various linear transformations of the target distribution. KBCC is observed to find, adapt, and use its relevant knowledge to significantly speed learning.


Copyright notice

Abstracts, papers, chapters, and other documents are posted on this site as an efficient way to distribute reprints and preprints. The respective authors and publishers of these works retain all of the copyrights to this material. Anyone copying, downloading, bookmarking, or printing any of these materials agrees to comply with all of the copyright terms. Other than having an electronic or printed copy for fair personal use, none of these works may be reposted, reprinted, or redistributed without the explicit permission of the relevant copyright holders.


To obtain a PDF preprint of this particular article, signal your agreement with these copyright terms by clicking on the statement below.


I agree with all of these copyright terms PDF 161KB