Abstract
A Support-Vector-Machine (SVM) learns for given 2-class-data a classifier that tries to achieve good generalisation by maximising the minimal margin between the two classes. The performance can be evaluated using cross-validation testing strategies. But in case of low sample size data, high dimensionality might lead to strong side-effects that can significantly bias the estimated performance of the classifier. On simulated data, we illustrate the effects of high dimensionality for cross-validation of both hard- and soft-margin SVMs. Based on the theoretical proofs towards infinity we derive heuristics that can be easily used to validate whether or not given data sets are subject to these constraints.
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Klement, S., Madany Mamlouk, A., Martinetz, T. (2008). Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios. In: Kůrková, V., Neruda, R., Koutník, J. (eds) Artificial Neural Networks - ICANN 2008. ICANN 2008. Lecture Notes in Computer Science, vol 5163. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87536-9_5
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DOI: https://doi.org/10.1007/978-3-540-87536-9_5
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