Standard
EMPRR : A high-dimensional EM-based piecewise regression algorithm. / Arumugam, Manimozhiyan; Scott, Stephen D.
Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04. red. / M. Kantardzic; O. Nasraoui; M. Milanova. 2004. s. 264-271 (Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Arumugam, M & Scott, SD 2004, EMPRR: A high-dimensional EM-based piecewise regression algorithm. i M Kantardzic, O Nasraoui & M Milanova (red), Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04. Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04, s. 264-271, 2004 International Conference on Machine Learning and Applications, ICMLA '04, Louisville, KY, USA, 16/12/2004.
APA
Arumugam, M., & Scott, S. D. (2004). EMPRR: A high-dimensional EM-based piecewise regression algorithm. I M. Kantardzic, O. Nasraoui, & M. Milanova (red.), Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04 (s. 264-271). Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04
Vancouver
Arumugam M, Scott SD. EMPRR: A high-dimensional EM-based piecewise regression algorithm. I Kantardzic M, Nasraoui O, Milanova M, red., Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04. 2004. s. 264-271. (Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04).
Author
Arumugam, Manimozhiyan ; Scott, Stephen D. / EMPRR : A high-dimensional EM-based piecewise regression algorithm. Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04. red. / M. Kantardzic ; O. Nasraoui ; M. Milanova. 2004. s. 264-271 (Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04).
Bibtex
@inproceedings{c4ef9803145a4e6b8782fc9c7706d7d6,
title = "EMPRR: A high-dimensional EM-based piecewise regression algorithm",
abstract = "We propose a novel general piecewise surface regression model that allows for arbitrary functions to be used in each piece, and arbitrary boundary surfaces between pieces. We also give an EM-based algorithm for this model, EMPRR, that scales to high dimensions. We compare EMPRR's performance with those of model trees and functional trees, two regression tree learning methods, on synthetic piecewise data and benchmark data sets. Our results show that EMPRR outperforms the other two methods on the synthetic data sets and performs competitively on the benchmark data sets while generating accurate and compact models.",
author = "Manimozhiyan Arumugam and Scott, {Stephen D.}",
year = "2004",
language = "English",
isbn = "0780388232",
series = "Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04",
pages = "264--271",
editor = "M. Kantardzic and O. Nasraoui and M. Milanova",
booktitle = "Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04",
note = "2004 International Conference on Machine Learning and Applications, ICMLA '04 ; Conference date: 16-12-2004 Through 18-12-2004",
}
RIS
TY - GEN
T1 - EMPRR
T2 - 2004 International Conference on Machine Learning and Applications, ICMLA '04
AU - Arumugam, Manimozhiyan
AU - Scott, Stephen D.
PY - 2004
Y1 - 2004
N2 - We propose a novel general piecewise surface regression model that allows for arbitrary functions to be used in each piece, and arbitrary boundary surfaces between pieces. We also give an EM-based algorithm for this model, EMPRR, that scales to high dimensions. We compare EMPRR's performance with those of model trees and functional trees, two regression tree learning methods, on synthetic piecewise data and benchmark data sets. Our results show that EMPRR outperforms the other two methods on the synthetic data sets and performs competitively on the benchmark data sets while generating accurate and compact models.
AB - We propose a novel general piecewise surface regression model that allows for arbitrary functions to be used in each piece, and arbitrary boundary surfaces between pieces. We also give an EM-based algorithm for this model, EMPRR, that scales to high dimensions. We compare EMPRR's performance with those of model trees and functional trees, two regression tree learning methods, on synthetic piecewise data and benchmark data sets. Our results show that EMPRR outperforms the other two methods on the synthetic data sets and performs competitively on the benchmark data sets while generating accurate and compact models.
UR - http://www.scopus.com/inward/record.url?scp=21244490829&partnerID=8YFLogxK
M3 - Article in proceedings
AN - SCOPUS:21244490829
SN - 0780388232
SN - 9780780388239
T3 - Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04
SP - 264
EP - 271
BT - Proceedings of the 2004 International Conference on Machine Learning and Applications, ICMLA '04
A2 - Kantardzic, M.
A2 - Nasraoui, O.
A2 - Milanova, M.
Y2 - 16 December 2004 through 18 December 2004
ER -
