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A flexible classification approach with optimal generalisation performance: support vector machines
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DOI：10.1016/S0169-7439(02)00046-1.png)
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Measuring a larger number of variables simultaneously becomes more and more easy and thus widespread. Obtaining a sufficient number of training samples or measurements, on the other hand, is still time-consuming and costly in many cases. Therefore, the problem of efficient learning from a limited training set becomes increasingly important. Support vector machines (SVM) as a recent approach to classification address this issue within the framework of statistical learning theory, They implement classifiers of an adjustable flexibility, which is automatically and in a principled way, optimised on the training data for a good generalisation performance. The approach is introduced and its learning behaviour examined, (C) 2002 Elsevier Science B.V. All rights reserved.
Keyword:
classification
generalisation
support vector machines
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