Spectral Techniques in Digital Logic
Spectral Techniques in Digital Logic
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Accuracy/Diversity and Ensemble MLP Classifier Design
IEEE Transactions on Neural Networks
Minimising Added Classification Error Using Walsh Coefficients
IEEE Transactions on Neural Networks
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If a binary decision is taken for each classifier in an ensemble, training patterns may be represented as binary vectors. For a two-class supervised learning problem this leads to a partially specified Boolean function that may be analysed in terms of spectral coefficients. In this paper it is shown that a vote which is weighted by the coefficients enables a fast ensemble classifier that achieves performance close to Bayes rate. Experimental evidence shows that effective classifier performance may be achieved with one epoch of training of an MLP using Levenberg-Marquardt with 64 hidden nodes.