Best-First Model Merging for Dynamic Learning and Recognition

"Best-first model merging" is a general technique for dynamically choosing the structure of a neural or related architecture while avoid(cid:173) ing overfitting. It is applicable to both leaming and recognition tasks and often generalizes significantly better than fixed structures. We dem(cid:173) onstrate the approach applied to the tasks of choosing radial basis func(cid:173) tions for function learning, choosing local affine models for curve and constraint surface modelling, and choosing the structure of a balltree or bumptree to maximize efficiency of access. This model is mostly wrong initially, but gets gradually better and better as data appears. The net deals with all data in much the same way and has no representation for the strength of evidence behind a certain conclusion.