Zugl.: Paderborn, Universiẗat, Diss., 1994.

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. Continuing, we take advantage of the a priori information of the labeled points to assign classes to clusters and proceed to predicting with the ensemble method new incoming ones. Thus, we can finally conclude classifying new data points according to the segmentation of the whole set and the association of its clusters to the classes. / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Aachen, Techn. Hochsch., Thesis (doctoral), 2000.

Mainz, Univ., Diss., 2005. / Erscheinungsjahr an der Haupttitelstelle: 2004

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 methods-all accessible through the software R-by the means of standard performance measures (classification error and mean squared error) which are also analyzed by the means of bias-variance decompositions. SVMs showed mostly good performances both on classification and regression tasks, but other methods proved to be very competitive. / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Zugl.: Ilmenau, Techn. Univ., Diss., 2009

University, Diss., 2003--Dortmund.

Universiẗat, Diss., 2004--Kiel.

In der Diplomarbeit wird die Weiterentwicklung eines Prototyps zur Nachrichtenklassifikation sowie die Integration in das bestehende Web-orientierte Informationssystem (WEBIS) beschrieben. Mit der entstandenen Software vorgenommene Klassifikationen werden vorgestellt und mit bisherigen Erkenntnissen verglichen.