  In this paper we propose an incremental learning strategy for import vector
machines (IVM), which is a sparse kernel logistic regression approach. We use
the procedure for the concept of self-training for sequential classification of
hyperspectral data. The strategy comprises the inclusion of new training
samples to increase the classification accuracy and the deletion of
non-informative samples to be memory- and runtime-efficient. Moreover, we
update the parameters in the incremental IVM model without re-training from
scratch. Therefore, the incremental classifier is able to deal with large data
sets. The performance of the IVM in comparison to support vector machines (SVM)
is evaluated in terms of accuracy and experiments are conducted to assess the
potential of the probabilistic outputs of the IVM. Experimental results
demonstrate that the IVM and SVM perform similar in terms of classification
accuracy. However, the number of import vectors is significantly lower when
compared to the number of support vectors and thus, the computation time during
classification can be decreased. Moreover, the probabilities provided by IVM
are more reliable, when compared to the probabilistic information, derived from
an SVM's output. In addition, the proposed self-training strategy can increase
the classification accuracy. Overall, the IVM and the its incremental version
is worthwhile for the classification of hyperspectral data.
