Classification Consequences of Preprocessing Radar Data

摘要

Support vector machines are classification algorithms based on quadratic programming that have been found to give excellent classification results on problems such as discriminating targets versus backgrounds. A key capability of these algorithms is that they need not require a preprocessing step to find feature vectors, yet preprocessing is still typically used. Preprocessing is still an important step in the classification process. We discuss what type of preprocessing steps are useful in improving the classifications results of support vector machines. We first give a short introduction to support vector machines. Then the algorithm is applied to the MSTAR radar data set. Several methods to preprocess the data are used before being sent to the support vector machine. The effect on the classification rate of the algorithm is then determined.