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Colin Fyfe
Researcher at University of the West of Scotland
Publications - 188
Citations - 2675
Colin Fyfe is an academic researcher from University of the West of Scotland. The author has contributed to research in topics: Artificial neural network & Projection pursuit. The author has an hindex of 28, co-authored 188 publications receiving 2571 citations.
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BookDOI
Intelligent Data Engineering and Automated Learning – IDEAL 2006
TL;DR: Learning and Information Processing -- Data Mining, Retrieval and Management -- Bioinformatics and Bio-inspired Models -- Agents and Hybrid Systems -- Financial Engineering -- Special Session on Nature-Inspired Date Technologies.
Journal ArticleDOI
A neural implementation of canonical correlation analysis
P. L. Lai,Colin Fyfe +1 more
TL;DR: A new method of performing Canonical Correlation Analysis with Artificial Neural Networks is derived and is applied to Becker's random dot stereogram data and shown to be extremely effective at detecting shift information.
Journal Article
Nonlinear Boosting Projections for Ensemble Construction
TL;DR: A novel approach for ensemble construction based on the use of nonlinear projections to achieve both accuracy and diversity of individual classifiers is proposed, which is less sensitive to noise in the data than boosting methods.
Journal ArticleDOI
Online clustering algorithms.
Wesam Ashour Barbakh,Colin Fyfe +1 more
TL;DR: A set of clustering algorithms whose performance function is such that the algorithms overcome one of the weaknesses of K-means, its sensitivity to initial conditions which leads it to converge to a local optimum rather than the global optimum.
Journal ArticleDOI
Class imbalance methods for translation initiation site recognition in DNA sequences
Nicolás García-Pedrajas,Javier Pérez-Rodríguez,María D. García-Pedrajas,Domingo Ortiz-Boyer,Colin Fyfe +4 more
TL;DR: This work approaches TIS recognition from a purely machine learning perspective, and applies the different methods that have been developed to deal with imbalanced datasets, showing an advantage of class imbalance methods with respect to the same methods applied without considering the class imbalance nature of the problem.