Speech Communication - Special issue on speech processing in adverse conditions
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Comparing support vector machines with Gaussian kernels to radialbasis function classifiers
IEEE Transactions on Signal Processing
Optimization of temporal filters for constructing robust features in speech recognition
IEEE Transactions on Audio, Speech, and Language Processing
How good are fuzzy If-Then classifiers?
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A recurrent neural fuzzy network for word boundary detection invariable noise-level environments
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
An online self-constructing neural fuzzy inference network and its applications
IEEE Transactions on Fuzzy Systems
Effect of rule weights in fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Self-adaptive neuro-fuzzy inference systems for classification applications
IEEE Transactions on Fuzzy Systems
Support vector learning for fuzzy rule-based classification systems
IEEE Transactions on Fuzzy Systems
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
IEEE Transactions on Fuzzy Systems
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems
IEEE Transactions on Fuzzy Systems
Support-vector-based fuzzy neural network for pattern classification
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
Support vector machines for histogram-based image classification
IEEE Transactions on Neural Networks
General fuzzy min-max neural network for clustering and classification
IEEE Transactions on Neural Networks
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
A study on reduced support vector machines
IEEE Transactions on Neural Networks
Digital Signal Processing
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A maximizing-discriminability-based self-organizing fuzzy network (MDSOFN) that can classify highly confusable patterns is proposed in this paper. The underlying notion of the proposed MDSOFN is to split the generation of fuzzy rules into linear discriminant analysis (LDA) and Gaussian mixturemodel (GMM). In LDA, the weights are updated by seeking directions that are efficient for discrimination. In GMM, parameter learning adopts the gradient-descent method to reduce the cost function. Since LDAderived fuzzy rules increase the discriminative capability among different classes, the proposed MDSOFN can classify highly confusable patterns. The effectiveness of the proposed MDSOFN is demonstrated by two classification problems. A detailed comparative performance analysis for the fuzzy networks using LDA, principal component analysis (PCA), and the support vector machine (SVM), with various noise types, is presented. Experimental results and theoretical analysis indicate that the LDA-derived fuzzy network performs better than the PCA-based fuzzy network and the SVM-based fuzzy network.