Fuzzy neural networks: a survey
Fuzzy Sets and Systems
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Advances in Large Margin Classifiers
Advances in Large Margin Classifiers
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
A Novel Measure for Quantifying the Topology Preservation of Self-Organizing Feature Maps
Neural Processing Letters
Rough-fuzzy functions in classification
Fuzzy Sets and Systems
Fuzzy least squares support vector machines for multiclass problems
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
ICCIMA '03 Proceedings of the 5th International Conference on Computational Intelligence and Multimedia Applications
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
A New Fuzzy Support Vector Machine Based on the Weighted Margin
Neural Processing Letters
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Comparison between error correcting output codes and fuzzy support vector machines
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Feature Selection using Fuzzy Support Vector Machines
Fuzzy Optimization and Decision Making
Stray Example Sheltering by Loss Regularized SVM and kNN Preprocessor
Neural Processing Letters
Margin calibration in SVM class-imbalanced learning
Neurocomputing
FSVM-CIL: fuzzy support vector machines for class imbalance learning
IEEE Transactions on Fuzzy Systems - Special section on computing with words
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue on Recent advances on machine learning and Cybernetics
Structural risk minimization over data-dependent hierarchies
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy classifier based on fuzzy support vector machine
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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Using class label fuzzification, this study develops the idea of refreshing the attitude of the difficult training examples and gaining a more robust classifier for large-margin support vector machines (SVMs). Fuzzification relaxes the specific hard-limited Lagrangian constraints of the difficult examples, extends the infeasible space of the canonical constraints for optimization, and reconfigures the consequent decision function with a wider margin. With the margin, a classifier capable of achieving a high generalization performance can be more robust. This paper traces the rationale for such a robust performance back to the changes of governing loss function. From the aspect of loss function, the reasons are causally explained. In the study, we also demonstrate a two-stage system for experiments to show the changes corresponding to the label fuzzification. The system first captures the difficult examples in the first-stage preprocessor, and assigns them various fuzzified class labels. Three types of membership functions, including a constant, a linear, and a sigmoidal membership function, are designated in the preprocessor to manipulate the within-class correlations of the difficult examples for reference of the fuzzification. The consequent performance benchmarks confirm the robust and generalized ability due to the label fuzzification. Since the change of y"i^' is fundamental, the idea may be transplanted to different prototypes of SVM.