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Authors: R. Vilalta ; F. Ocegueda-Hernandez and C. Bagaria

Affiliation: University of Houston, United States

Keyword(s): Model selection, Classification, Supervised learning, VC dimension.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Data Mining ; Databases and Information Systems Integration ; Enterprise Information Systems ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems

Abstract: A key concept in model selection is to understand how model complexity can be modified to improve in generalization performance. One design alternative is to increase model complexity on a single global model (by increasing the degree of a polynomial function); another alternative is to combine multiple local models into a composite model. We provide a conceptual study that compares these two alternatives. Following the Structural Risk Minimization framework, we derive bounds for the maximum number of local models or folds below which the composite model remains at an advantage with respect to the single global model. Our results can be instrumental in the design of learning algorithms displaying better control over model complexity.

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Paper citation in several formats:
Vilalta, R.; Ocegueda-Hernandez, F. and Bagaria, C. (2010). A CONCEPTUAL STUDY OF MODEL SELECTION IN CLASSIFICATION - Multiple Local Models vs One Global Model. In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-674-021-4; ISSN 2184-433X, SciTePress, pages 113-118. DOI: 10.5220/0002733601130118

@conference{icaart10,
author={R. Vilalta. and F. Ocegueda{-}Hernandez. and C. Bagaria.},
title={A CONCEPTUAL STUDY OF MODEL SELECTION IN CLASSIFICATION - Multiple Local Models vs One Global Model},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2010},
pages={113-118},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002733601130118},
isbn={978-989-674-021-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - A CONCEPTUAL STUDY OF MODEL SELECTION IN CLASSIFICATION - Multiple Local Models vs One Global Model
SN - 978-989-674-021-4
IS - 2184-433X
AU - Vilalta, R.
AU - Ocegueda-Hernandez, F.
AU - Bagaria, C.
PY - 2010
SP - 113
EP - 118
DO - 10.5220/0002733601130118
PB - SciTePress

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