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https://www.sciencedirect.com/science/article/pii/S0165011402005146
Sep 01, 2003 · Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems. In this paper, we introduce the use of SVM for multivariate fuzzy linear and nonlinear regression models. Using the basic idea underlying SVM for multivariate fuzzy regressions gives computational efficiency of getting solutions.Cited by: 171
https://www.researchgate.net/publication/4043316_Fuzzy_support_vector_machine_for_regression_estimation
Download Citation Fuzzy support vector machine for regression estimation The theory of support vector machine (SVM), as a tool of pattern classification and regression estimation, draws much ...
https://www.sciencedirect.com/science/article/pii/S0360544217301822
A fuzzy regression functions with support vector machines approach is proposed. The approach is robust against outlier observations and over-fitting problem. Estimation accuracy of the model is superior to several existent alternatives.Cited by: 21
https://pdfs.semanticscholar.org/1d1a/d762addd571ba728ea24786f75757b8b2c22.pdf
the system. This fuzzy regression model might be very useful for finding a fuzzy structure in an evaluation sys-tem. The Support Vector Machines (SVMs), developed at AT&T Bell Laboratories by Vapnik and co-works [3], [12], have been very successful in pattern classification and function estimation problems for crisp data. It is
https://www.researchgate.net/publication/222414404_Support_vector_fuzzy_regression_machines
Support vector machine (SVM) has been very successful in pattern recognition and function estimation problems. In this paper, we introduce the use of SVM for multivariate fuzzy linear …
https://link.springer.com/chapter/10.1007%2F978-3-319-30165-5_6
Mar 26, 2016 · Abstract. This paper presents methods of estimating fuzzy regression models based on support vector machines. Starting from the approaches known from the literature and dedicated to triangular fuzzy numbers and based on linear and quadratic loss, a new method applying loss function based on the Trutschnig distance is proposed.Cited by: 1
https://www.researchgate.net/publication/281361135_Hybrid_fuzzy_support_vector_regression_analysis
In this paper, a new approach called Hybrid Fuzzy Support Vector Regression (HF-SVR) is introduced for the linear and non-linear fuzzy regression modeling. According to the proposed algorithm,...
https://www.researchgate.net/publication/311928183_Support_vector_machine_and_fuzzy_logic
The article gives a short description of the history of the Support Vector Machine (SVM) method and fuzzy logic and their main parameters. It describes how SVM can be used for classification and...
http://uni-obuda.hu/journal/Menyhart_Szabolcsi_69.pdf
Abstract: The article gives a short description of the history of the Support Vector Machine (SVM) method and fuzzy logic and their main parameters. It describes how SVM can be used for classification and regression calculation.
https://www.researchgate.net/publication/256309499_Fuzzy_Support_Vector_Machines
Support vector machine for classification based on fuzzy training data. Expert Systems with Applications 37(4), 3495–3498]. The authors have claimed that their proposed program is a classical ...
https://en.wikipedia.org/wiki/Support-vector_machine
The soft-margin support vector machine described above is an example of an empirical risk minimization (ERM) algorithm for the hinge loss. Seen this way, support vector machines belong to a natural class of algorithms for statistical inference, and many of its unique features are due to …
https://www.researchgate.net/publication/256309499_Fuzzy_Support_Vector_Machines
Support vector machines (SVMs) are versatile learning models which are used for both classification and regression. Several authors have reported successful applications of SVM in a wide range of ...
https://www.sciencedirect.com/science/article/pii/S0020025507003209
A new fuzzy system modeling (FSM) approach that identifies the fuzzy functions using support vector machines (SVM) is proposed. This new approach is structurally different from the fuzzy rule base approaches and fuzzy regression methods. It is a new alternate version of the earlier FSM with fuzzy functions approaches.Cited by: 132
https://pdfs.semanticscholar.org/1d1a/d762addd571ba728ea24786f75757b8b2c22.pdf
model and fuzzy regression model, where the SVM learning theory characterizes properties of learning machines which enable them to generalize well and the fuzzy set theory provides an effective means of capturing the approximate, inexact nature of real world. Keywords: Support Vector Machines (SVMs), Support Vector Regression, Fuzzy Regression. 1.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849760/
Experiment 4: as an instance of the rule-base generated by fuzzy support vector machine, we first apply SNR feature selection method on leukemia dataset and select three genes and then we set up fuzzy support vector machine classifier on those genes. We explore some characteristics of the model by taking a deeper look at the rule-base.Cited by: 10
https://www.researchgate.net/publication/311928183_Support_vector_machine_and_fuzzy_logic
The history of the Support Vector Machine (SVM) method and fuzzy logic and its use for classification and regression calculation is described in this [10] paper. Electric vehicles' battery ...
https://www.sciencedirect.com/science/article/pii/S0957417407004435
In this paper, we propose a novel approach, termed as regularized least squares fuzzy support vector regression, to handle financial time series forecasting. Two key problems in financial time series forecasting are noise and non-stationarity.Cited by: 108
http://uni-obuda.hu/journal/Menyhart_Szabolcsi_69.pdf
J. Menyhárt et al. Support Vector Machine and Fuzzy Logic – 210 – Figure 3 The ε – insensitive loss function [6] [14] [36] 3.4 New Approach of SVM Used for Operating Conditions In the case of electric and autonomus vehicles the range gets a higher priority.
https://www.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
https://link.springer.com/chapter/10.1007%2F978-3-319-30165-5_6
Mar 26, 2016 · Abstract. This paper presents methods of estimating fuzzy regression models based on support vector machines. Starting from the approaches known from the literature and dedicated to triangular fuzzy numbers and based on linear and quadratic loss, a new method applying loss function based on the Trutschnig distance is proposed.Cited by: 1
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