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Titlebook: Makro?konomik; Theorie, Empirie und Uwe Westphal Textbook 1994Latest edition Springer-Verlag Heidelberg 1994 Arbeitsmarkt.Geld.Geldmenge.Ge

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11#
發(fā)表于 2025-3-23 11:03:41 | 只看該作者
12#
發(fā)表于 2025-3-23 15:10:32 | 只看該作者
Uwe Westphalms are based on the assumption that the nuclei center should have larger responses than their surroundings in the probability map of the pathological image, which in turn transforms the detection or localization problem into finding the local maxima on the probability map. However, all the existing
13#
發(fā)表于 2025-3-23 21:25:05 | 只看該作者
14#
發(fā)表于 2025-3-24 01:41:14 | 只看該作者
fect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based a
15#
發(fā)表于 2025-3-24 04:38:55 | 只看該作者
Uwe Westphalst CT data is however challenging because of low contrast and clutter. Sliding window detectors using traditional features easily get confused by similar structures like muscles and vessels. It recently has been proposed to combine segmentation and detection to improve the detection performance. Fea
16#
發(fā)表于 2025-3-24 06:38:29 | 只看該作者
Uwe Westphaltical methods to analyze small data. The first volume reviewed subjects like optimal scaling, neural networks, factor analysis, partial least squares, discriminant analysis, canonical analysis, and fuzzy modeling. This second volume includes various clustering models, support vector machines, Bayesi
17#
發(fā)表于 2025-3-24 12:37:15 | 只看該作者
18#
發(fā)表于 2025-3-24 17:33:51 | 只看該作者
19#
發(fā)表于 2025-3-24 22:10:44 | 只看該作者
Uwe Westphalssible without special computationally intensive methods.CliMachine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scali
20#
發(fā)表于 2025-3-25 01:52:11 | 只看該作者
ssible without special computationally intensive methods.CliMachine learning is concerned with the analysis of large data and multiple variables. However, it is also often more sensitive than traditional statistical methods to analyze small data. The first volume reviewed subjects like optimal scali
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