Best-First Model Merging for Dynamic Learning and Recognition
Title | Best-First Model Merging for Dynamic Learning and Recognition |
Publication Type | Technical Report |
Year of Publication | 1992 |
Authors | Omohundro, S. |
Other Numbers | 709 |
Abstract | "Best-first model merging" is a general technique for dynamically choosing the structure of a neural or related architecture while avoiding overfitting. It is applicable to both learning and recognition tasks and often generalizes significantly better than fixed structures. We demonstrate the approach applied to the tasks of choosing radial basis functions for function learning, choosing local affine models for curve and constraint surface modeling, and choosing the structure of a balltree or bumptree to maximize efficiency of access. |
URL | http://www.icsi.berkeley.edu/ftp/global/pub/techreports/1992/tr-92-004.pdf |
Bibliographic Notes | ICSI Technical Report TR-92-004 |
Abbreviated Authors | S. M. Omohundro |
ICSI Publication Type | Technical Report |