The nature of statistical learning theory
The nature of statistical learning theory
A fuzzy classifier system for evolutionary learning of robot behaviors
Applied Mathematics and Computation - Special issue on articficial life and robotics
Theory and Practice of Uncertain Programming
Theory and Practice of Uncertain Programming
Support vector fuzzy regression machines
Fuzzy Sets and Systems - Theme: Learning and modeling
Training algorithms for fuzzy support vector machines with noisy data
Pattern Recognition Letters
A weighted fuzzy classifier and its application to image processing tasks
Fuzzy Sets and Systems
Robust fuzzy relational classifier incorporating the soft class labels
Pattern Recognition Letters
A multilayered neuro-fuzzy classifier with self-organizing properties
Fuzzy Sets and Systems
Evolving fuzzy classifiers using different model architectures
Fuzzy Sets and Systems
Expert Systems with Applications: An International Journal
Support vector machine for classification based on fuzzy training data
Expert Systems with Applications: An International Journal
Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The forecasting model based on fuzzy novel ν-support vector machine
Expert Systems with Applications: An International Journal
Incorporating Fuzzy Membership Functions into the Perceptron Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy SVM with a New Fuzzy Membership Function to Solve the Two-Class Problems
Neural Processing Letters
Relaxed constraints support vector machines for noisy data
Neural Computing and Applications - Special Issue on WCCI2008
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Fuzzy min-max neural networks. I. Classification
IEEE Transactions on Neural Networks
An Approach to Estimating Product Design Time Based on Fuzzy -Support Vector Machine
IEEE Transactions on Neural Networks
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Support vector machines SVMs have been very successful in pattern recognition and function estimation problems. When SVMs are used for classification, the inputs of the training example are real-valued and the outputs are class label y = ±1. However, in practice, the training examples usually belong to a class with certain fuzzy membership, therefore it is important to consider uncertain class label for classification problems. For this purpose, this paper introduces the new concept of fuzzy hyperplane, and constructs the fuzzy classifiers based on fuzzy support vector machines. At the end of the paper, we apply our new methods to medical diagnosis problems.