Anton Zamolotskikh

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Producing estimates of classification confidence is surprisingly difficult. One might expect that classifiers that can produce numeric classification scores (e.g. k-Nearest Neighbour or Naive Bayes) could readily produce confidence estimates based on thresholds. In fact, this proves not to be the case, probably because these are not proba-bilistic(More)
Because of their sound theoretical underpinnings, Support Vector Machines (SVMs) have very impressive performance in classification. However, the use of SVMs is constrained by the fact that the affinity measure that is used to build the classifier must produce a kernel matrix that is positive semi-definite (PSD). This is normally not a problem, however many(More)
This paper presents False Positive-Critical Classifiers Comparison a new technique for pairwise comparison of classifiers that allow the control of bias. An evaluation of Na&#239;ve Bayes, <i>k</i>-Nearest Neighbour and Support Vector Machine classifiers has been carried out on five datasets containing unsolicited and legitimate e-mail messages to confirm(More)
The paper presents a comparison of the data-based and Gene Ontology (GO)-based approaches to cluster validation methods for gene microarray analysis. We apply a homogeneous approach to obtaining metrics from different GO-based similarity measures and a normalization of validation index values, that allows us to compare them to each other as well as to(More)
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