Local Learning
What is Local Learning?

"Local methods assign a weight to each training observation that regulates its
influence on the training process.
This weight depends upon the location of the training point in the input
variable space relative to that of the point to be predicted.
Training observations closer to the prediction point generally receive
higher weights."
from:
Jerome H. Friedman, abstract for talk on "Intelligent Local Learning For
Prediction in High Dimensions", International Conference on Artificial Neural
Networks (ICANN 95), October 913, 1995, Paris, France.
Reviews, Overviews, and Surveys
An overview of work on local learning algorithms is given by:
Atkeson, C. G., Moore, A. W., & Schaal, S. (submitted).
Locally Weighted Learning.
Artificial Intelligence Review.
An overview of local learning applied to robots is given by:
Atkeson, C. G., Moore, A. W., & Schaal, S. (submitted).
Locally Weighted Learning for Control.
Artificial Intelligence Review.
Overviews of local regression are given in:
Cleveland, W. S. and C. Loader.
Smoothing by Local Regression: Principles and Methods.
and
Fan, J.
Local Modeling
A book is available:
Jianqing Fan and Irene Gijbels
Local Polynomial Modeling and its Applications
Chapman and Hall, London, 1996.
Software for local regression is available:

LOCFIT

Recent ATT/Bell Labs work.

LOESS

Older ATT/Bell Labs work.
Also available from
ftp://ftp.netlib.org/a/loess

LOWESS

Very old ATT/Bell Labs work.

AUTON

Andrew Moore's work at CMU.

RFWR

Stefan Schaal's work at ATR, GT, and MIT.

Biostat

Local polynomial regression fitting with Epanechnikov weights or
ridging, and MATLAB Smoothing Toolbox from the Department of Biostatistics
at unizh.ch.

NoLoEss

Locally parametric regression estimation:
DOS program by Andrzej S. Kozek.
Papers

Locally Weighted Learning
Atkeson, C. G., Moore, A. W., & Schaal, S.

Locally Weighted Learning for Control
Atkeson, C. G., Moore, A. W., & Schaal, S.

Smoothing by Local Regression: Principles and Methods
Cleveland, W. S. and C. Loader.

Function Approximation with Neural Networks and Local Methods: Bias, Variance,
and Smoothness
Steve Lawrence, Ah Chung Tsoi, and Andrew D. Back.
(also at
http://www.elec.uq.edu.au/~lawrence)

A New Nonparmetric Estimation Method: Local and Nonlinear
Andrzej S. Kozek.

Fast Computation of Auxiliary Quantities in Local Polynomial Regression
B. A. Turlach and M. P. Wand.

Variance properties of local polynomials
Burkhardt Seifert and Theo Gasser, Nov. 1994.

Finite sample variance of local polynomials
Burkhardt Seifert and Theo Gasser, May 1994.

Flexible Metric Nearest Neighbor Classification
Jerome H. Friedman, Technical Report (Nov. 1994).

Discriminant Adaptive Nearest Neighbor Classification
Hastie, T. J. and Tibshirani, R., Technical Report (Dec. 1994).

Handwritten Digit Recognition via Deformable Prototypes
Hastie, T. J. and Tibshirani, R., AT&T Bell Laboratories Technical Report 1994.

Learning Prototype Models for Tangent Distance
NIPS proceedings, 1994.

Is Learning the nth Thing Any Easier Than Learning The First?
Sebastian Thrun. Application of memorybased learning
to reinforcement learning.
To appear in: Advances in Neural
Information Processing Systems (NIPS) 8.

Discovering Structure in Multiple Learning Tasks: The TC Algorithm
Sebastian Thrun and Joseph O'Sullivan. Application of memorybased learning
to reinforcement learning.
To appear in: International Conference on Machine Learning 1996.

Scaling up Average Reward Reinforcement Learning by Approximating the Domain
Models and the Value Function
Tadepalli, P. and Ok, D. Submitted to ICML96. Application of local linear
regression to reinforcement learning.
People and Places
Web Stuff
Search Keywords

local* + learn*

local* + regression

local* + weight*

local + function approximation

local* + model*

LWR

LOCFIT

LOESS

LOWESS

memory + learn*

instance + learn*

exemplar + learn*

lazy + learn*

least commitment learn*
If you have any comments or hotlinks to add about projects related
to what we do, please let
cga@cc.gatech.edu
know.
Back to
Chris Atkeson's home page