Header logo is

Assessing the quality of learned local models


Conference Paper


An approach is presented to learning high dimensional functions in the case where the learning algorithm can affect the generation of new data. A local modeling algorithm, locally weighted regression, is used to represent the learned function. Architectural parameters of the approach, such as distance metrics, are also localized and become a function of the query point instead of being global. Statistical tests are given for when a local model is good enough and sampling should be moved to a new area. Our methods explicitly deal with the case where prediction accuracy requirements exist during exploration: By gradually shifting a "center of exploration" and controlling the speed of the shift with local prediction accuracy, a goal-directed exploration of state space takes place along the fringes of the current data support until the task goal is achieved. We illustrate this approach with simulation results and results from a real robot learning a complex juggling task.

Author(s): Schaal, S. and Atkeson, C. G.
Book Title: Advances in Neural Information Processing Systems 6
Pages: 160-167
Year: 1994
Editors: Cowan, J.;Tesauro, G.;Alspector, J.
Publisher: Morgan Kaufmann

Department(s): Autonomous Motion
Bibtex Type: Conference Paper (inproceedings)

Address: San Mateo, CA
Cross Ref: p866
Note: clmc
URL: http://www-clmc.usc.edu/publications/S/schaal-NIPS1994.pdf


  title = {Assessing the quality of learned local models},
  author = {Schaal, S. and Atkeson, C. G.},
  booktitle = {Advances in Neural Information Processing Systems 6},
  pages = {160-167},
  editors = {Cowan, J.;Tesauro, G.;Alspector, J.},
  publisher = {Morgan Kaufmann},
  address = {San Mateo, CA},
  year = {1994},
  note = {clmc},
  crossref = {p866},
  url = {http://www-clmc.usc.edu/publications/S/schaal-NIPS1994.pdf}