The grand tour: a tool for viewing multidimensional data
SIAM Journal on Scientific and Statistical Computing
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Gaining insights into support vector machine pattern classifiers using projection-based tour methods
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Fuzzy classifiers based on kernel discriminant analysis
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Quality of classification explanations with PRBF
Neurocomputing
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We design a method for weighting linear support vectormachine classifiers or random hyperplanes, to obtain classifierswhose accuracy is comparable to the accuracy of anon-linear support vector machine classifier, and whose resultscan be readily visualized. We conduct a simulationstudy to examine how our weighted linear classifiers behavein the presence of known structure. The results show thatthe weighted linear classifiers might perform well comparedto the non-linear support vector machine classifiers, whilethey are more readily interpretable than the non-linear classifiers.