Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
An improved fuzzy neural network based on T-S model
Expert Systems with Applications: An International Journal
Data mining with a simulated annealing based fuzzy classification system
Pattern Recognition
Structure Automatic Change in Neural Network
ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks
OWA-weighted based clustering method for classification problem
Expert Systems with Applications: An International Journal
Incremental local linear fuzzy classifier in fisher space
EURASIP Journal on Advances in Signal Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on cybernetics and cognitive informatics
FR3: a fuzzy rule learner for inducing reliable classifiers
IEEE Transactions on Fuzzy Systems
Contourlet-based mammography mass classification using the SVM family
Computers in Biology and Medicine
Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1
Expert Systems with Applications: An International Journal
Online boosting for vehicle detection
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
A maximizing-discriminability-based self-organizing fuzzy network for classification problems
IEEE Transactions on Fuzzy Systems
Construction of a neuron-fuzzy classification model based on feature-extraction approach
Expert Systems with Applications: An International Journal
Combination of EEG complexity and spectral analysis for epilepsy diagnosis and seizure detection
EURASIP Journal on Advances in Signal Processing
A self-organizing fuzzy neural network based on a growing-and-pruning algorithm
IEEE Transactions on Fuzzy Systems
An Iterative Method for Deciding SVM and Single Layer Neural Network Structures
Neural Processing Letters
Generalized augmentation of multiple kernels
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Digital Signal Processing
Detecting RNA sequences using two-stage SVM classifier
LSMS'07 Proceedings of the 2007 international conference on Life System Modeling and Simulation
Contourlet-based mammography mass classification
ICIAR'07 Proceedings of the 4th international conference on Image Analysis and Recognition
An efficient classification approach for large-scale mobile ubiquitous computing
Information Sciences: an International Journal
A granular neural network: Performance analysis and application to re-granulation
International Journal of Approximate Reasoning
Robust support vector machine-trained fuzzy system
Neural Networks
Fast classification for large data sets via random selection clustering and Support Vector Machines
Intelligent Data Analysis
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Fuzzy neural networks (FNNs) for pattern classification usually use the backpropagation or C-cluster type learning algorithms to learn the parameters of the fuzzy rules and membership functions from the training data. However, such kinds of learning algorithms usually cannot minimize the empirical risk (training error) and expected risk (testing error) simultaneously, and thus cannot reach a good classification performance in the testing phase. To tackle this drawback, a support-vector-based fuzzy neural network (SVFNN) is proposed for pattern classification in this paper. The SVFNN combines the superior classification power of support vector machine (SVM) in high dimensional data spaces and the efficient human-like reasoning of FNN in handling uncertainty information. A learning algorithm consisting of three learning phases is developed to construct the SVFNN and train its parameters. In the first phase, the fuzzy rules and membership functions are automatically determined by the clustering principle. In the second phase, the parameters of FNN are calculated by the SVM with the proposed adaptive fuzzy kernel function. In the third phase, the relevant fuzzy rules are selected by the proposed reducing fuzzy rule method. To investigate the effectiveness of the proposed SVFNN classification, it is applied to the Iris, Vehicle, Dna, Satimage, Ijcnn1 datasets from the UCI Repository, Statlog collection and IJCNN challenge 2001, respectively. Experimental results show that the proposed SVFNN for pattern classification can achieve good classification performance with drastically reduced number of fuzzy kernel functions.