We consider the issue of knowledge (re-)representation in the context of learning transfer and present a sub-symbolic 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.
Subjects: 12. Machine Learning and Discovery; 11. Knowledge Representation
Submitted: Feb 2, 2008