learning transfer, neural networks
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.
BYU ScholarsArchive Citation
Ventura, Dan A., "Sub-symbolic Re-representation to Facilitate Learning Transfer" (2008). All Faculty Publications. 905.
Physical and Mathematical Sciences
© 2008 AAAI.
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