The assessment of the performance of learners by means of benchmark experiments is established exercise. In practice, benchmark studies are a tool to compare the performance of several competing algorithms for a certain learning problem. Cross-validation or resampling techniques are commonly...

In this paper we introduce a mixed approach for the semi-supervised data problem. Our approach consists of an ensemble unsupervised learning part where the labeled and unlabeled points are segmented into clusters.(...)

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling and flexible specification of design parameters.(...)

Support Vector Machines (SVMs) are rarely benchmarked against other classification or regression methods. We compare a popular SVM implementation (libsvm) to 16 classification methods and 9 regression methodsall accessible through the software Ruby the means of standard performance measures...

In order to circumvent common difficulties in exchanging statistical data between heterogeneous applications (format incompatibilities, technocentric data representation), we introduce an XML-based markup language for statistical data, called StatDataML. After comparing StatDataML to other data...

In this study, we empirically analyze the determinants of heterogeneity in rating assessments across different segments of the European loan market. We conduct a benchmarking analysis using rating information on European corporate obligors from nine major Austrian banks that have a large share...

The validation of credit rating systems has recently attracted particular interest both from banks and their supervisors as well as from academic research. Whereas the main interest has been focused on backtesting methods, alternative approaches such as benchmarking are of growing importance....