Adaptive pattern recognition and neural networks
Adaptive pattern recognition and neural networks
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Stacking with Multi-response Model Trees
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Using Discriminant Analysis for Multi-class Classification
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Intrusion detection using hierarchical neural networks
Pattern Recognition Letters
Genetic algorithms for linear feature extraction
Pattern Recognition Letters
Identification of Nonlinear Systems Using Neural Networks and Polynomial Models: A Block-Oriented Approach (Lecture Notes in Control and Information Sciences)
Pattern Recognition Letters
Engineering Applications of Artificial Intelligence
A classification technique based on radial basis function neural networks
Advances in Engineering Software
Simultaneous evolution of neural network topologies and weights for classification and regression
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural and statistical classifiers-taxonomy and two case studies
IEEE Transactions on Neural Networks
Modeling the manifolds of images of handwritten digits
IEEE Transactions on Neural Networks
Face recognition with radial basis function (RBF) neural networks
IEEE Transactions on Neural Networks
A target-based color space for sea target detection
Applied Intelligence
| Hi-index | 0.00 |
Polynomial neural networks have been known to exhibit useful properties as classifiers and universal approximators. In this study, we introduce a concept of polynomial-based radial basis function neural networks (P-RBF NNs), present a design methodology and show the use of the networks in classification problems. From the conceptual standpoint, the classifiers of this form can be expressed as a collection of "if-then" rules. The proposed architecture uses two essential development mechanisms. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of condition parts of the rules while the corresponding conclusions of the rules are formed by some polynomials. A detailed learning algorithm for the P-RBF NNs is developed. The proposed classifier is applied to two-class pattern classification problems. The performance of this classifier is contrasted with the results produced by the "standard" RBF neural networks. In addition, the experimental application covers a comparative analysis including several previous commonly encountered methods such as standard neural networks, SVM, SOM, PCA, LDA, C4.5, and decision trees. The experimental results reveal that the proposed approach comes with a simpler structure of the classifier and better prediction capabilities.