Paper available on incremental local learning

[Stefan Schaal]

The following paper is available by anonymous FTP or WWW:


                     FROM ISOLATION TO COOPERATIONION:
                AN ALTERNATIVE VIEW OF A SYSTEM OF EXPERTS

                 Stefan Schaal and Christopher G. Atkeson
                           submitted to NIPS'95

        We  introduce a constructive, incremental learning system
        for  regression  problems that models data  by  means  of
        locally  linear experts. In contrast to other approaches,
        the  experts are trained independently and do not compete
        for  data during learning. Only when a prediction  for  a
        query  is  required do the experts cooperate by  blending
        their  individual predictions. Each expert is trained  by
        minimizing a penalized local cross validation error using
        second  order methods. In this way, an expert is able  to
        adjust the size and shape of the receptive field in which
        its predictions are valid, and also to adjust its bias on
        the  importance of individual input dimensions. The  size
        and  shape  adjustment corresponds  to  finding  a  local
        distance  metric, while the bias adjustment  accomplishes
        local  dimensionality  reduction.  We  derive  asymptotic
        results  for  our method. In a variety of simulations  we
        demonstrate the properties of the algorithm with  respect
        to  interference,  learning speed,  prediction  accuracy,
        feature   detection,   and  task   oriented   incremental
        learning.

              

-------------------------
The paper is 8 pages long, requires 2.3 MB of memory (uncompressed), 
and is ftp-able as:
	ftp://ftp.cc.gatech.edu/people/sschaal/schaal-NIPS95.ps.gz
or can be accessed through:
	http://www.cc.gatech.edu/fac/Stefan.Schaal/
	http://www.hip.atr.co.jp/~sschaal/

Comments are most welcome.