Takane, T., Oshima-Takane, Y., & Shultz, T. R (2003).
Neural network simulations by cascade correlation and knowledge-based cascade
correlation networks. In T. Higuchi, Y. Iba, & M. Ishiguro (Eds.), Proceedings of Science of Modeling: The 30th
Anniversary Meeting of the Information Criterion (AIC), (pp. 245-254). Report
on Research and Education 17.
Cascade correlation (CC) has proven to be an effective tool for simulating human learning. One important class of problem solving tasks can be thought of as establishing appropriate connections between inputs and outputs. A CC network initially attempts to solve the task with a minimal network configuration, but when the task cannot be solved, it is powered up by recruiting a hidden unit to capture the uncaptured aspects of the input-output relationship until a satisfactory degree of performance is reached. Knowledge-based CC (KBCC) has a similar mechanism, but instead of recruiting hidden units, it can recruit other networks previously trained with similar tasks. In this paper we demonstrate the usefulness of these network tools for simulating learning behavior by human subjects.
Abstracts, papers, chapters, and other documents are posted on this site as an efficient way to distribute reprints. 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 reprint of this particular article, signal your agreement with these copyright terms by clicking on the statement below.