Generating classifier outputs of fixed accuracy and diversity
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
A single-sensor hand geometry and palmprint verification system
WBMA '03 Proceedings of the 2003 ACM SIGMM workshop on Biometrics methods and applications
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Some new features for protein fold prediction
ICANN/ICONIP'03 Proceedings of the 2003 joint international conference on Artificial neural networks and neural information processing
Generalized Needleman-Wunsch algorithm for the recognition of T-cell epitopes
Expert Systems with Applications: An International Journal
Margin-based ensemble classifier for protein fold recognition
Expert Systems with Applications: An International Journal
Feature Fusion Using Multiple Component Analysis
Neural Processing Letters
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Predicting the three-dimensional structure of a protein from its amino acid sequence is an important problem in bioinformatics and a challenging task for machine learning algorithms. Given (numerical) features, one of the existing machine learning techniques can be then applied to learn and classify proteins represented by these features. We show that combining Fisher's linear classifier and K-Local Hyperplane Distance Nearest Neighbor we obtain an error rate lower than previously published in the literature.