Optimally Trained Regression Trees and Occam's Razor

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SYSNO ASEP	0404694
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Optimally Trained Regression Trees and Occam's Razor
Author(s)	Savický, Petr (UIVT-O)_{SAI, RID, ORCID} Klaschka, Jan (UIVT-O)_{RID, SAI, ORCID}
Source Title	COMPSTAT 2002. Proceedings in Computational Statistics / Härdle W. ; Rönz B.. - Heidelberg : PhysicaVerlag, 2002 - ISBN 3-7908-1517-9
Pages	s. 479-484
Number of pages	6 s.
Action	COMPSTAT 2002
Event date	24.08.2002-28.08.2002
VEvent location	Berlin
Country	DE - Germany
Event type	WRD
Language	eng - English
Country	DE - Germany
Keywords	regression trees ; recursive partitioning ; optimization ; dynamic programming ; bottom-up algorithms ; generalization ; Occam's razor
Subject RIV	BA - General Mathematics
R&D Projects	GA201/00/1482 GA ČR - Czech Science Foundation (CSF)
CEZ	1030915
UT WOS	000179942900073
Annotation	Two bottom-up algorithms growing regression trees with the minimum mean squared error on the training data given the number of leaves are described. As demonstraded by the results of experiments with simulated data, the trees resulting from the optimization algorithms may have not only better, but also worse generalization properties than the trees grown by traiditional methods. This phenomenon is discussed from the point of view of the Occam's razor principle.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2003

Number of the records: 1