Martina Uray

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Incremental subspace methods have proven to enable efficient training if large amounts of training data have to be processed or if not all data is available in advance. In this paper we focus on incremental LDA learning which provides good classification results while it assures a compact data representation. In contrast to existing incremental LDA methods(More)
BACKGROUND With the introduction of tissue microarrays (TMAs) researchers can investigate gene and protein expression in tissues on a high-throughput scale. TMAs generate a wealth of data calling for extended, high level data management. Enhanced data analysis and systematic data management are required for traceability and reproducibility of experiments(More)
In air traffic management (ATM) all necessary operations (tactical planing, sector configuration, required staffing, runway configuration , routing of approaching aircrafts) rely on accurate measurements and predictions of the current weather situation. An essential basis of information is delivered by weather radar images (WXR), which, unfortunately ,(More)
In this paper we consider the limitations of Linear Discriminative Analysis (LDA) when applying it for large-scale problems. Since LDA was originally developed for two-class problems the obtained transformation is sub-optimal if multiple classes are considered. In fact, the separability between the classes is reduced, which decreases the classification(More)
Enzymes are becoming increasingly important tools for synthesizing and modifying fine and bulk chemicals. The availability of biocatalysts which fulfil the requirements of industrial processes is often limited. Recruiting suited enzymes from natural (e.g. metagenomes) and artificial (e.g. directed evolution) biodiversity is based on screening libraries of(More)
  • Martina Uray, Ao Univ, Dipl.-Ing Techn Otto Laback, Carl Schurz, Dt.-Amerik Politiker
  • 2003
Ideale sind wie Sterne. Man kann sie nicht erreichen, aber man kann sich an ihnen orientieren. Bei Unklarheiten die digitale Bildverarbeitung betreend, wird auf die Nach-schlagewerke [Jäh91], [Hab95], [Hab87], [GW92] und [SHB99] verwiesen. Auch die technischen Berichte [Bra01], [SVMW02] und [Lad02] liefern einen guten Einblick in dieses Fachgebiet.
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