title: |
On the convergence of HLMS Algorithm |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
| pages: | 817 - 821 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Murillo Javier, Serge Guillaume, Elizabeth Tapia, Bulacio Pilar |
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publication date: |
July 2011 |
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keywords: |
multicriteria, fuzzy integrals, HLMS, convergence |
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abstract: |
In multicriteria decision making, the study of attribute
contributions is crucial to attain correct decisions.
Fuzzy measures allow a complete description
of the joint behavior of attribute subsets. However,
the determination of fuzzy measures is often
hard. A common way to identify fuzzy measures is
HLMS (Heuristic Least Mean Squares) algorithm.
In this paper, the convergence of the HLMS algorithm
is analyzed. First, we show that the learning
rate parameter ( ) dominates the convergence of
HLMS. Second, we provide an upper bound for
that guarantees HLMS convergence. In addition,
a toy example shows the descriptive power of fuzzy
measures versus the poverty of individual measures. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |