Support vector approaches for engine knock detection

We show the application of large margin classifiers to the real world problem of engine knock detection. Large margin classifiers, like support vector machines (SVM) or the Adatron, promise a good generalization performance. Furthermore, the support vector approach has some bounds (e.g. for generalization error and learning convergence) which give this technique a more firm background than the neural network leaning algorithms. One drawback of the SVM, especially the Adatron, is that they tend to produce classification systems which need large computational effort for recall. This is caused by the fact that support vectors are normally sparse, but their number of calls is high. Therefore, we propose here a method which prunes (removes) support vectors that are less important. By an adjustment of the training data and remaining steps of the classifier a performance degradation is avoided