Keywords

learning transfer, neural networks

Abstract

We consider the issue of knowledge (re-)representation in the context of learning transfer and present a subsymbolic approach for effecting such transfer. Given a set of data, manifold learning is used to automatically organize the data into one or more representational transformations, which are then learned with a set of neural networks. The result is a set of neural filters that can be applied to new data as re-representation operators. Encouraging preliminary empirical results elucidate the approach and demonstrate its feasibility, suggesting possible implications for the broader field of creativity.

Original Publication Citation

Dan Ventura, "A Sub-symbolic Model of the Cognitive Processes of Re-representation and Insight", Creative Intelligent Systems: Papers from the AAAI Spring Symposium, 28.