A Cross-Validation Approach to Knowledge Transfer for SVM Models in the Learning Using Privileged Information Paradigm

Sammanfattning: The learning using privileged information paradigm has allowed support vector machine models to incorporate privileged information, variables available in the training set but not in the test set, to improve predictive ability. The consequent introduction of the knowledge transfer method has enabled a practical application of support vector machine models utilizing privileged information. This thesis describes a modified knowledge transfer method inspired by cross-validation, which unlike the current standard knowledge transfer method does not create the knowledge transfer function and the approximated privileged features used in the support vector machines on the same observations. The modified method, the robust knowledge transfer, is described and evaluated versus the standard knowledge transfer method and is shown to be able to improve the predictive performance of the support vector machines for both binary classification and regression.