Application of support vector machines on the basis of the first Hungarian bankruptcy model

In our study we rely on a data mining procedure known as support vector machine (SVM) on the database of the first Hungarian bankruptcy model. The models constructed are then contrasted with the results of earlier bankruptcy models with the use of classification accuracy and the area under the ROC curve. In using the SVM technique, in addition to conventional kernel functions, we also examine the possibilities of applying the ANOVA kernel function and take a detailed look at data preparation tasks recommended in using the SVM method (handling of outliers). The results of the models assembled suggest that a significant improvement of classification accuracy can be achieved on the database of the first Hungarian bankruptcy model when using the SVM method as opposed to neural networks

Physics and chemistry of hydrogen in the vacancies of semiconductors

Hydrogen is well known to cause electrical passivation of lattice vacancies in semiconductors. This effect follows from the chemical passivation of the dangling bonds. Recently it was found that H in the carbon vacancy of SiC forms a three-center bond with two silicon neighbors in the vacancy, and gives rise to a new electrically active state. In this paper we examine hydrogen in the anion vacancies of BN, AlN, and GaN. We find that three-center bonding of H is quite common and follows clear trends in terms of the second-neighbor distance in the lattice, the typical (two-center) hydrogen-host-atom bond length, the electronegativity difference between host atoms and hydrogen, as well as the charge state of the vacancy. Three-center bonding limits the number of H atoms a nitrogen vacancy can capture to two, and prevents electric passivation in GaAs as well