In this paper, experiments on various classifiers and combining these classifiers are done, reported and analyzed. Combining the classifiers means having the single classifiers support each other in making a decision, instead of having only a single classifier’s decision as the final decision. The base experiment involves, both, applying different single classifiers on a dataset and applying th...

When more than a single classifier has been trained for the same recognition problem the question arises how this set of classifiers may be combined into a final decision rule. Several fixed combining rules are used that depend on the output values of the base classifiers only. They are almost always suboptimal. Usually, however, training sets are available. They may be used to calibrate the ba...

On the Semantic Web, there has been increasing demand for a ruleslike expressivity that goes beyond OWL-DL. Efforts of combining rules languages and description logics usually produce undecidable formalisms, unless constrained in a specific way. We look at one of the most expressive but decidable such formalisms proposed: DL-safe rules, and present a tableaux-based algorithm for query answering...

Next generation wireless systems are being designed to provide ubiquitous broadband link access to information infrastructure. Diversity techniques play a vital role in supporting such high speed connections over radio channels by mitigating the detrimental effects of multiuser interference and multipath fading. Equal gain combining (EGC) diversity receiver is of practical interest because of i...

This thesis consists of five included papers plus an introduction. The majority of the papers are devoted to performance analysis of an adaptive coded modulation (ACM) scheme based on multidimensional trellis codes. Primarily, single-user systems exploiting spatial diversity are analysed, but results are also presented for a multiuser system exploiting multiuser diversity. The performance of th...