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Learning Fuzzy Measures

Version 2 2024-06-06, 04:27
Version 1 2019-06-28, 14:57
chapter
posted on 2024-06-06, 04:27 authored by Gleb BeliakovGleb Beliakov, Simon JamesSimon James, JZ Wu
© 2020, Springer Nature Switzerland AG. This chapter is a key contribution of this work in which various computational approaches to learning fuzzy measures are described. The learning problem is framed from the perspective of data fitting, where we aim to define a model that interpolates or approximates a set of observed or user-specified instances. Fitting is performed with respect to different metrics, and by solving different convex and non-convex optimisation problems. The computational complexity of fuzzy measures is addressed by using simplifying assumptions, in particular the notion of k-order fuzzy measures and the software packages implementing the presented fitting approaches are also described.

History

Volume

382

Pagination

205-239

ISSN

1434-9922

eISSN

1860-0808

ISBN-13

9783030153045

Publication classification

BN Other book chapter, or book chapter not attributed to Deakin

Publisher

Springer

Place of publication

Cham, Switzerland

Title of book

Discrete Fuzzy Measures

Series

Studies in Fuzziness and Soft Computing

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