The cascade-correlation learning architecture
Advances in neural information processing systems 2
Constructing hidden units using examples and queries
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Original Contribution: Stacked generalization
Neural Networks
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Data selection for support vector machine classifiers
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
A global optimum approach for one-layer neural networks
Neural Computation
Machine Learning
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Square Unit Augmented, Radially Extended, Multilayer Perceptrons
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
A comparative assessment of classification methods
Decision Support Systems
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Vector quantization using information theoretic concepts
Natural Computing: an international journal
Combining Feature Selection and Local Modelling in the KDD Cup 99 Dataset
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part I
Local modeling classifier for microarray gene-expression data
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part III
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In this paper, a novel supervised architecture for binary classification based on local modelling and information theory is described. The architecture is composed of two steps: in the first one, a separating borderline between the two classes is piecewise constructed by a set of centroids calculated by a modified clustering algorithm, based on information theory; each of these centroids define a region where, in the second step of the proposed architecture, a hyperplane is constructed and adjusted by means of one-layer neural networks. This new method allows for binary classification while maintaining adequate use of computational resources, a common problem for machine learning methods. The proposed architecture is applied over classical benchmark classification problems and data sets, and its results are compared with those obtained by other well-known statistical and machine learning classifiers.