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Why LASSO, Ridge Regression, and EN

Explanation Based on Soft Computing

verfasst von
Woraphon Yamaka, Hamza Alkhatib, Ingo Neumann, Vladik Kreinovich
Abstract

In many practical situations, observations and measurement results are consistent with many different models–i.e., the corresponding problem is ill-posed. In such situations, a reasonable idea is to take into account that the values of the corresponding parameters should not be too large; this idea is known as regularization. Several different regularization techniques have been proposed; empirically the most successful are LASSO method, when we bound the sum of absolute values of the parameters, ridge regression method, when we bound the sum of the squares, and a EN method in which these two approaches are combined. In this paper, we explain the empirical success of these methods by showing that these methods can be naturally derived from soft computing ideas.

Organisationseinheit(en)
Geodätisches Institut
Externe Organisation(en)
Chiang Mai University
University of Texas at El Paso
Typ
Aufsatz in Konferenzband
Seiten
123-130
Anzahl der Seiten
8
Publikationsdatum
27.06.2021
Publikationsstatus
Elektronisch veröffentlicht (E-Pub)
Peer-reviewed
Ja
ASJC Scopus Sachgebiete
Artificial intelligence
Elektronische Version(en)
https://scholarworks.utep.edu/cs_techrep/1465/ (Zugang: Offen)
https://doi.org/10.1007/978-3-030-77094-5_12 (Zugang: Geschlossen)