Overfitting and undercomputing in machine learning
ACM Computing Surveys (CSUR)
Constructing fuzzy models by product space clustering
Fuzzy model identification
Fuzzy functions and their fundamental properties
Fuzzy Sets and Systems
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Approximative fuzzy rules approaches for classification with hybrid-GA techniques
Information Sciences: an International Journal - Recent advances in genetic fuzzy systems
Incorporating Invariances in Support Vector Learning Machines
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
Granular Modeling: The Synergy of Granular Computing and Fuzzy Logic
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Toward a generalized theory of uncertainty (GTU): an outline
Information Sciences—Informatics and Computer Science: An International Journal
Agent-based architecture for designing hybrid control systems
Information Sciences: an International Journal
A genetic fuzzy agent using ontology model for meeting scheduling system
Information Sciences: an International Journal
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
A similarity measure for fuzzy rulebases based on linguistic gradients
Information Sciences: an International Journal
Development of a systematic methodology of fuzzy logic modeling
IEEE Transactions on Fuzzy Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Validation criteria for enhanced fuzzy clustering
Pattern Recognition Letters
The theoretical foundations of statistical learning theory based on fuzzy number samples
Information Sciences: an International Journal
Supremum metric on the space of fuzzy sets and common fixed point theorems for fuzzy mappings
Information Sciences: an International Journal
Gaussian case-based reasoning for business failure prediction with empirical data in China
Information Sciences: an International Journal
Increasing accuracy of two-class pattern recognition with enhanced fuzzy functions
Expert Systems with Applications: An International Journal
Evolution of Fuzzy System Models: An Overview and New Directions
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
A New Classifier Design with Fuzzy Functions
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Information Sciences: an International Journal
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Decision making with imprecise parameters
International Journal of Approximate Reasoning
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Revenue forecasting using a least-squares support vector regression model in a fuzzy environment
Information Sciences: an International Journal
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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. SVM is applied to determine the support vectors for each fuzzy cluster obtained by fuzzy c-means (FCM) clustering algorithm. Original input variables, the membership values obtained from the FCM together with their transformations form a new augmented set of input variables. The performance of the proposed system modeling approach is compared to previous fuzzy functions approaches, standard SVM, LSE methods using an artificial sparse dataset and a real-life non-sparse dataset. The results indicate that the proposed fuzzy functions with support vector machines approach is a feasible and stable method for regression problems and results in higher performances than the classical statistical methods.