Demonstrating the stability of support vector machines for classification

In this paper, we deal with the stability of support vector machines (SVMs) in classification tasks. We decompose the average prediction error of SVMs into the bias and the variance terms, and we define the aggregation effect. By estimating the aforementioned terms with bootstrap smoothing techniques, we demonstrate that support vector machines are stable classifiers. To investigate the stability of the SVM several experiments were conducted. The first experiment deals with face detection. The second experiment conducted is related to the binary classification of three artificially generated data sets stemming from known distributions and an additional synthetic data set known as “Waveform”. Finally, in order to support our claim on the stability of SVMs, two more binary classification experiments were carried out on the “Pime Indian Diabetes” and the “Wisconsin Breast Cancer” data sets. In general, bagging is not expected to improve the classification accuracy of SVMs.