Multilayer feedforward networks are universal approximators
Neural Networks
Pattern classification: a unified view of statistical and neural approaches
Pattern classification: a unified view of statistical and neural approaches
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Discovering Useful Concept Prototypes for Classification Based on Filtering and Abstraction
IEEE Transactions on Pattern Analysis and Machine Intelligence
On the Discriminatory Power of Adaptive Feed-Forward Layered Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fingerprint and Speaker Verification Decisions Fusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
DeEPs: A New Instance-Based Lazy Discovery and Classification System
Machine Learning
Benchmarking a Reduced Multivariate Polynomial Pattern Classifier
IEEE Transactions on Pattern Analysis and Machine Intelligence
Classification-based objective functions
Machine Learning
Training a reciprocal-sigmoid classifier by feature scaling-space
Machine Learning
Learning Algorithms for Nonparametric Solution to the Minimum Error Classification Problem
IEEE Transactions on Computers
Discriminative learning for minimum error classification [patternrecognition]
IEEE Transactions on Signal Processing
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
IEEE Transactions on Neural Networks
Classification ability of single hidden layer feedforward neural networks
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
RBF neural network center selection based on Fisher ratio class separability measure
IEEE Transactions on Neural Networks
A novel radial basis function neural network for discriminant analysis
IEEE Transactions on Neural Networks
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
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This paper presents a novel quadratic error-counting network for pattern classification. Two computational issues namely, the network learning issue and the classification error-counting issue have been addressed. Essentially, a linear series functional approximation to network structure and a smooth quadratic error-counting cost function were proposed to resolve these two computational issues within a single framework. Our analysis shows that the quadratic error-counting objective can be related to the least-squares-error objective by adjusting the class-specific normalization factors. The binary classification network is subsequently extended to cater for multicategory problems. An extensive empirical evaluation validates the usefulness of proposed method.