Reliability of Cross-Validation for SVMs in High-Dimensional, Low Sample Size Scenarios
نویسندگان
چکیده
A Support-Vector-Machine (SVM) learns for given 2-classdata 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 crossvalidation of both hardand 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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