Publication Details
Title: Best-First Model Merging for Dynamic Learning and Recognition
Author: S. M. Omohundro
Group: ICSI Technical Reports
Date: January 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-004.pdf
Overview:
"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.
Bibliographic Information:
ICSI Technical Report TR-92-004
Bibliographic Reference:
S. M. Omohundro. Best-First Model Merging for Dynamic Learning and Recognition. ICSI Technical Report TR-92-004, January 1992
Author: S. M. Omohundro
Group: ICSI Technical Reports
Date: January 1992
PDF: ftp://ftp.icsi.berkeley.edu/pub/techreports/1992/tr-92-004.pdf
Overview:
"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.
Bibliographic Information:
ICSI Technical Report TR-92-004
Bibliographic Reference:
S. M. Omohundro. Best-First Model Merging for Dynamic Learning and Recognition. ICSI Technical Report TR-92-004, January 1992