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https://www.christianthiel.com/publications/F2SVMS.pdf
Fuzzy-Input Fuzzy-Output One-Against-All Support Vector Machines Christian Thiel, Stefan Scherer and Friedhelm Schwenker Institute of Neural Information Processing, University of Ulm, 89069 Ulm, Germany
https://link.springer.com/chapter/10.1007%2F978-3-540-74829-8_20
Abstract. We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers.Cited by: 33
https://www.sciencedirect.com/science/article/pii/S0885230812000423
The standard methods of choice for comparison were naive Bayes classifier (NB), giving a rough baseline, and standard crisp support vector machines (SVMs) utilizing the same radial basis function (RBF) kernel as the fuzzy-input fuzzy-output support vector machines (F 2 SVMs). Both SVM types utilize the one-against-one multi-class paradigm.Cited by: 73
https://www.semanticscholar.org/paper/Fuzzy-Input-Fuzzy-Output-One-Against-All-Support-Thiel-Scherer/ebf734f1185b9fa984e7f0dd8c7fc88719ad1256
We present a novel approach for Fuzzy-Input Fuzzy-Output classification. One-Against-All Support Vector Machines are adapted to deal with the fuzzy memberships encoded in fuzzy labels, and to also give fuzzy classification answers. The mathematical background for the modifications is given. In a benchmark application, the recognition of emotions in human speech, the accuracy of our F2-SVM ...
https://www.researchgate.net/publication/256309499_Fuzzy_Support_Vector_Machines
A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes.
https://www.sciencedirect.com/science/article/pii/S0020025507003209
Fuzzy functions with support vector machines (FF-SVM) The proposed FF-SVM approach is a variation of FF-LSE [27] method and structurally different from other FSM methods described in Section 2 . The new FF-SVM searches for the relationship between inputs and output using SVR for each fuzzy partition identified by FCM [2] .Cited by: 131
http://ict.usc.edu/pubs/Investigating%20Fuzzy-Input%20Fuzzy-Output%20Support%20Vector%20Machines%20for%20Robust%20Voice%20Quality%20Classification.pdf
The fuzzy-input fuzzy-output support vector machine (F2SVM) introduced in (Thiel et al., 2007; Borasca et al., 2006; Thiel, 2009) is an ideal candidate for this type of task receiving a fuzzy membership label as input with the features for training and producing fuzzy memberships2 as output.
https://www.researchgate.net/publication/228877879_Error_correcting_output_codes_vs_fuzzy_support_vector_machines
In this paper, first we prove that for one-against-all formulation, support vector machines with continuous decision functions are equivalent to fuzzy support vector machines with minimum and ...
https://link.springer.com/chapter/10.1007%2F978-3-319-11656-3_14
Abstract. In this paper a novel approach to fuzzy support vector machines (SVM) in multi-class classification problems is presented. The proposed algorithm has the property to benefit from fuzzy labeled data in the training phase and can determine fuzzy memberships for input data.Cited by: 3
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849760/
Fuzzy support vector machine is a fuzzy rule-based model in which membership functions are reference functions with location transformation and given input x → determines output class label by equation (9) in which K (x →, z J ⃗) is a Mercer kernel defined by equation (8).Cited by: 10
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