A classification study of marine phytoplankton on the base of improved SVM

The SVM (Support Vector Machine) is superior to other artificial neural network (such as the BP network) in classification. And its rapid development and the wide application are due to the introduction of the concept of soft margin. However, the traditional soft margin SVM gives the same misclassification costs for the various sample data, thus the processing results of the real data are not satisfactory. In this paper, the traditional SVM soft margin algorithm is improved by paying different costs for different misclassification. So the correct classification rate is further increased. And the BP network, RBF (Radial Basis Function) network, SVM and the improved SVM are applied to classify marine phytoplankton (enteromorpha) and the classification results are analyzed comparatively.