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A Mixed Ensemble Approach for the Semi-Supervised ProblemDimitriadou, Evgenia, Weingessel, Andreas, Hornik, Kurt January 2002 (has links) (PDF)
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"
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Benchmarking Support Vector MachinesMeyer, David, Leisch, Friedrich, Hornik, Kurt January 2002 (has links) (PDF)
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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Analyse des Mikroschlafs mit Methoden der computergestützten IntelligenzSommer, David January 2009 (has links)
Zugl.: Ilmenau, Techn. Univ., Diss., 2009
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Bundling classifiers with an application to glaucoma diagnosisHothorn, Torsten. Unknown Date (has links) (PDF)
University, Diss., 2003--Dortmund.
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Mathematische Mustererkennung und Hidden-Markov-ModelleWillert, Lars. Unknown Date (has links) (PDF)
Universiẗat, Diss., 2004--Kiel.
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Nachrichtenklassifikation als Komponente in WEBISKrellner, Björn 29 September 2006 (has links) (PDF)
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.
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Efficient multi-class object detectionZehnder, Philipp January 2009 (has links)
Zugl.: Zürich, Techn. Hochsch., Diss., 2009
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Statistical learning with similarity and dissimilarity functionsLuxburg, Ulrike von. Unknown Date (has links) (PDF)
Techn. University, Diss., 2004--Berlin.
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Nachrichtenklassifikation als Komponente in WEBISKrellner, Björn 25 September 2006 (has links)
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.
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