Neural network design and the complexity of learning
Neural network design and the complexity of learning
An overview of neural networks: early models to real world systems
An introduction to neural and electronic networks
Adaptive Sampling Methods for Scaling Up Knowledge Discovery Algorithms
Data Mining and Knowledge Discovery
Feature Selection Via Mathematical Programming
INFORMS Journal on Computing
Handbook of data mining and knowledge discovery
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Nearest-Neighbor Methods in Learning and Vision: Theory and Practice (Neural Information Processing)
Information and Complexity in Statistical Modeling
Information and Complexity in Statistical Modeling
A Class-Based Feature Selection Method for Ensemble Systems
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
A multitask learning model for online pattern recognition
IEEE Transactions on Neural Networks
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
A class-specific ensemble feature selection approach for classification problems
Proceedings of the 48th Annual Southeast Regional Conference
Iterative generation of higher-order nets in polynomial time using linear programming
IEEE Transactions on Neural Networks
A neural-network learning theory and a polynomial time RBF algorithm
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
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This paper presents methods for training pattern (prototype) selection, class-specific feature selection and classification for automated learning. For training pattern selection, we propose a method of sampling that extracts a small number of representative training patterns (prototypes) from the dataset. The idea is to extract a set of prototype training patterns that represents each class region in a classification problem. In class-specific feature selection, we try to find a separate feature set for each class such that it is the best one to separate that class from the other classes. We then build a separate classifier for that class based on its own feature set. The paper also presents a new hypersphere classification algorithm. Hypersphere nets are similar to radial basis function (RBF) nets and belong to the group of kernel function nets. Polynomial time complexity of the methods is proven. Polynomial time complexity of learning algorithms is important to the field of neural networks. Computational results are provided for a number of well-known datasets. None of the parameters of the algorithm were fine tuned for any of the problems solved and this supports the idea of automation of learning methods. Automation of learning is crucial to wider deployment of learning technologies.