Type: RI Seminar
Who: Andrew W. Moore, Carnegie Mellon University
Topic: Learning control with kernel-based function
approximators and intense cross-validation
Dates: 29-Apr-94
Time: 3:30 pm - 5:00 pm
Place: Adamson Wing Auditorium in Baker Hall
Duration: 90 Minutes
Host: Yangsheng Xu (xu@cs.cmu.edu)
Appointment: Lalit Katragadda (lalit@cs.cmu.edu)
ABSTRACT
A learning controller can benefit considerably from explicitly
remembering every experience in its lifetime. In this talk I will
discuss how locally weighted regression and its variants can be used
for controlling robots and other complex systems. I will then
describe how models of forward and inverse dynamics learned together
can provide a robust training regime.
While learning control, data can sometimes be more plentiful than in
typical function approximation applications. Cheaply available data
can be used to further increase the autonomy of the learning
controller. Intense searches can be carried out for finding
parameters, regression-orders and subsets of features which minimize
cross-validation error. Much of this talk will concern methods for
doing this with computational efficiency, broadly based on the idea of
quickly cutting off cross-validation computations which are not
predicted to give useful results. Improvements are then given,
including (1)~the use of blocking to quickly spot near-identical
models, and (2)~schemata search: a new method for quickly finding
families of relevant features. Experiments are presented for robot
data and noisy synthetic datasets. The new algorithms speed up
computation without sacrificing reliability, and in some cases are
more reliable than conventional techniques.
If time permits I will also discuss a new kd-tree algorithm for
accelerating the cost of locally weighted regression predictions.
This algorithm permits points to be added incrementally to the tree
with logarithmic cost, and is particularly appropriate for input
spaces with many variables or for local weighting functions which
are not very local (perhaps using a significant fraction of the
stored datapoints).
Cboards: general
PostedBy: Yangsheng.Xu on 22-Apr-1994 at 18:10 from IUS4.IUS.CS.CMU.EDU
LastMod: Friday, 22-Apr-94 at 18:16