A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Support Vector Machines and the Bayes Rule in Classification
Data Mining and Knowledge Discovery
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
A machine learning methodology for the analysis of workplace accidents
International Journal of Computer Mathematics - Recent Advances in Computational and Applied Mathematics in Science and Engineering
Hybrid computational models for the characterization of oil and gas reservoirs
Expert Systems with Applications: An International Journal
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In this work, we create a quality map of a slate deposit, using the results of an investigation based on surface geology and continuous core borehole sampling. Once the quality of the slate and the location of the sampling points have been defined, different kinds of support vector machines (SVMs)-SVM classification (multiclass one-against-all), ordinal SVM and SVM regression-are used to draw up the quality map. The results are also compared with those for kriging. The results obtained demonstrate that SVM regression and ordinal SVM are perfectly comparable to kriging and possess some additional advantages, namely, their interpretability and control of outliers in terms of the support vectors. Likewise, the benefits of using the covariogram as the kernel of the SVM are evaluated, with a view to incorporating the problem association structure in the feature space geometry. In our problem, this strategy not only improved our results but also implied substantial computational savings.