Luís Torgo, Joaquim Pinto da Costa  Home/Search
Context
Related
| ncc.up.pt/~ltorgo/...cplr_msl2000.ps.gz Cached: PS.gz PS PDF This document uses CoBlitz to cache paper downloads. If your firewall is blocking outgoing connections to port 3125, you can use these links to download local copies.
Image Update HelpPS.gz PS PDF From: ncc.up.pt/~ltorgo/Pape...list_pub (more) (Enter author homepages) |
Rate this article:      (best)Comment on this article |
Abstract: This paper presents a new method that deals with a supervised learning task usually known as multivariate regression. The main distinguishing feature of this new technique is the use of a clustering method to obtain sub-sets of the training data before the learning phase. After this "resampling" process a different regression model is fitted to each found cluster. We call the resulting method clustered partial linear regression. (Update)
Active bibliography (related documents): More All
0.5: Predicting the Density of Algae Communities using Local Regression .. - Torgo (Correct)
0.2: Concept Lattice based Composite Classifiers for High.. - XIE, HSU, LIU, LEE (2002) (Correct)
0.2: The need for Improved Reinforcement Learning Techniques in.. - Wunsch (Correct)
Similar documents based on text: More All
0.9: A Comparative Study of Reliable Error Estimators for Pruning.. - Torgo (1998) (Correct)
0.8: Regression by Classification - Torgo, Gama (1996) (Correct)
0.8: Kernel Regression Trees - Torgo (Correct)
BibTeX entry: (Update)
@inproceedings{ torgo00clustered,
author = "Lu{\'{i}}z Torgo and Joaquim Pinto da Costa",
title = "Clustered Partial Linear Regression",
booktitle = "Machine Learning: {ECML} 2000, 11th European Conference on Machine Learning, Barcelona, Catalonia, Spain, May 31 - June 2, 2000, Proceedings",
volume = "1810",
publisher = "Springer, Berlin",
pages = "426--436",
year = "2000",
url = "citeseer.ist.psu.edu/torgo00clustered.html" }
Citations (may not include all citations):1535 Cambridge University Press (context) - Press, Teukolsky et al. - 1992
1262 Classification and Regression Trees (context) - Breiman, Friedman et al. - 1984
657 Bagging predictors - Breiman - 1996
500 Experiments with a new boosting algorithm - Freund, Shapire - 1996
273 Multivariate adaptive regression splines (context) - Friedman - 1991
166 Applied Regression Analysis (context) - Drapper, Smith - 1981
153 Autoclass: a bayesian classification system (context) - Cheeseman, Kelly et al. - 1988
135 theory and results (context) - Cheesman, Stutz - 1995
126 Applied Nonparametric Regression (context) - Hardle - 1990
87 Smooth regression analysis (context) - Watson - 1964
50 Artificial Intelligence Review (context) - Atkeson, Moore et al. - 1997
42 Local regression: automatic kernel carpentry - Hastie, Loader - 1993
39 Integrating quantitative and qualitative discovery in the AB.. (context) - Falkenhainer, Michalski - 1990
22 Theory of Probability and its Applications (context) - Nadaraya - 1964
10 The strength of weak learnability (context) - Shapire - 1990
5 Kluwer Academic Publishers (context) - Aha - 1997
2 Two techniques for estimating treatment effects in the prese.. (context) - Spiegelman - 1976
1 Regression through genetic algorithm driven clustering (context) - Devogelaere, Van Bael et al. - 1999
Documents on the same site (http://www.ncc.up.pt/~ltorgo/Papers/list_pub.html): More
Learning in Distributed Systems and Multi-Agent Environments - Brazdil Gams (1991) (Correct)
Data Fitting with Rule-Based Regression - Torgo (1995) (Correct)
Error Estimators for Pruning Regression Trees - Torgo (Correct)
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC