Communications of the ACM - Special issue on parallelism
Neural networks for pattern recognition
Neural networks for pattern recognition
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence
An equivalence between sparse approximation and support vector machines
Neural Computation
Generalization performance of support vector machines and other pattern classifiers
Advances in kernel methods
General and Efficient Multisplitting of Numerical Attributes
Machine Learning
Artificial nonmonotonic neural networks
Artificial Intelligence
Indexing the Solution Space: A New Technique for Nearest Neighbor Search in High-Dimensional Space
IEEE Transactions on Knowledge and Data Engineering
Knowledge discovery and data mining in biological databases
The Knowledge Engineering Review
Improved heterogeneous distance functions
Journal of Artificial Intelligence Research
Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement
IEEE Transactions on Fuzzy Systems
Selecting radial basis function network centers with recursive orthogonal least squares training
IEEE Transactions on Neural Networks
Ischemia detection with a self-organizing map supplemented by supervised learning
IEEE Transactions on Neural Networks
Efficient and interpretable fuzzy classifiers from data with support vector learning
Intelligent Data Analysis
Classification process analysis of bioinformatics data with a support vector fuzzy inference system
NN'07 Proceedings of the 8th Conference on 8th WSEAS International Conference on Neural Networks - Volume 8
Efficient and interpretable fuzzy classifiers from data with support vector learning
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
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The application of neuro-fuzzy systems to domains involving prediction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. Traditional distances are inadequate to provide information about the proximity between the symbolic patterns. This work proposes a new architecture of neurofuzzy systems, the Symbolic Adaptive Neuro Fuzzy Inference System (SANFIS) that utilizes effectively a statistically extracted distance measure. The learning approach is a hybrid one and consists of a sequence of steps some of which are essential and some are used in order to optimize further the performance. Initially, a Statistical Distance Metric space is computed from the information provided with the training set. The premise parameters are subsequently evaluated with a three-phase Instance Based Learning (IBL) scheme that estimates the input membership function centers and spreads and constructs the corresponding fuzzy rules. The first phase of this scheme explores heuristic approaches that can uncover information for the relative importance and the reliability of the examples. The second phase exploits this information and extracts an adequate subset of the training patterns for the construction of the fuzzy rules. The concept of fuzzy adaptive subsethood is used at the third phase, for the reduction of the number of the fuzzy sets used as input membership functions. The consequent parameters are estimated with an efficient linear least squares formulation. The obtained performances from the SANFIS trained with the hybrid learning methods are significantly better than the traditional nearest neighbour Instance Based Learning schemes and compares well with advanced neural designs. At the same time SANFIS provides an enhanced explanation ability with the construction of a few interpretable rules.