Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach

Chiang, Jung-Hsien; Hao, Pei-Yi

doi:10.1109/tfuzz.2003.817839

Cited by 172 publications

(67 citation statements)

References 33 publications

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“…Another typology of FSVM regards the extraction of fuzzy rules from the trained SVM model and a lot of works were been proposed (Chiang & Hao, 2004;Chen & Wang, 2003;Chaves et al, 2005;Castro et al, 2007).…”

Section: Fuzzy Based Approachesmentioning

confidence: 99%

Fuzzy Inference System for Data Processing in Industrial Applications

Cateni¹,

Colla²

2012

Fuzzy Inference System - Theory and Applications

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Section: Fuzzy Based Approachesmentioning

confidence: 99%

Fuzzy Inference System for Data Processing in Industrial Applications

Cateni¹,

Colla²

2012

Fuzzy Inference System - Theory and Applications

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“…The SVR is a non-parametric, regularized, and non-linear regression tool which has been applied to optimal control, time-series prediction, interval regression analysis, determination of initial structures for fuzzy neural networks, radial basis function networks, etc. (Hong and Hwang 2002;Chiang and Hao 2004;Juang and Hsieh 2009;Khemchandani et al 2009;Yang et al 2009). …”

Section: Introductionmentioning

confidence: 99%

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

Roldán

Martínez-Moreno

2011

Soft Comput

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Least-squares technique is well-known and widely used to determine the coefficients of a explanatory model from observations based on a concept of distance. Traditionally, the observations consist of pairs of numeric values. However, in many real-life problems, the independent or explanatory variable can be observed precisely (for instance, the time) and the dependent or response variable is usually described by approximate values, such as ''about £300'' or ''approximately $500'', instead of exact values, due to sources of uncertainty that may affect the response. In this paper, we present a new technique to obtain fuzzy regression models that consider triangular fuzzy numbers in the response variable. The procedure solves linear and non-linear problems and is easy to compute in practice and may be applied in different contexts. The usefulness of the proposed method is illustrated using simulated and real-life examples.

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“…In essence, we encounter methods to extract fuzzy-rule based classifiers from SV machines for both settings, i.e. classification [6,7] and regression [8]. The objectives are different compared to the first direction.…”

Section: Introductionmentioning

confidence: 99%

On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs

Moewes¹,

Kruse²

2011

Proceedings of the 7th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT-2011)

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In this paper we reason about the usefulness of two recent trends in fuzzy methods in machine learning. That is, we discuss both fuzzy support vector machines (FSVMs) and the extraction of fuzzy rules from SVMs. First, we show that an FSVM is identical to a special type of SVM. Second, we categorize and analyze existing approaches to obtain fuzzy rules from SVMs. Finally, we question both trends and conclude with more promising alternatives.

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Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach

Cited by 172 publications

References 33 publications

Fuzzy Inference System for Data Processing in Industrial Applications

Fuzzy Inference System for Data Processing in Industrial Applications

A fuzzy regression model based on distances and random variables with crisp input and fuzzy output data: a case study in biomass production

On the usefulness of fuzzy SVMs and the extraction of fuzzy rules from SVMs

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