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Titlebook: Innere Medizin; Krankheitslehre Kran Christoph Lanzend?rfer,Resi Lanzend?rfer,Christian Book 1998 Springer-Verlag Berlin Heidelberg 1998 Ba

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21#
發(fā)表于 2025-3-25 03:44:33 | 只看該作者
den Kapitels.Vergleich der Vor- und Nachteile der Algorithme.Sie m?chten endlich wissen, was sich hinter Schlagworten wie ?Data Science“ und ?Machine Learning“ eigentlich verbirgt – und was man alles damit anstellen kann? Auf allzu viel Mathematik würden Sie dabei aber gern verzichten? Dann sind Sie
22#
發(fā)表于 2025-3-25 08:46:58 | 只看該作者
23#
發(fā)表于 2025-3-25 15:01:36 | 只看該作者
24#
發(fā)表于 2025-3-25 17:53:21 | 只看該作者
Christoph Lanzend?rfer,Resi Lanzend?rfer,Harald Schardelmann,Christian SubbeFederation of Oassification Societies (IFCS-96), which was held in Kobe, Japan, from March 27 to 30,1996. The volume covers a wide range of topics and perspectives in the growing field of data science, including theoretical and methodological advances in domains relating to data gathering, classific
25#
發(fā)表于 2025-3-25 22:04:57 | 只看該作者
26#
發(fā)表于 2025-3-26 01:42:46 | 只看該作者
27#
發(fā)表于 2025-3-26 07:21:24 | 只看該作者
28#
發(fā)表于 2025-3-26 11:41:31 | 只看該作者
Christoph Lanzend?rfer,Resi Lanzend?rfer,Harald Schardelmann,Christian Subbeefinitively a solution to reduce the storage capacities and, as a result, authorizes to increase the penetration of the photovoltaic units on the power grid. We present the first results of an interdisciplinary research project which involves researchers in energy, meteorology, and data mining, addr
29#
發(fā)表于 2025-3-26 13:40:46 | 只看該作者
Christoph Lanzend?rfer,Resi Lanzend?rfer,Harald Schardelmann,Christian Subbeetworks and support applications. Mining patterns in such distributed and dynamic environment is a challenging task, because centralization of data is not feasible. In this paper, we have proposed a distributed classification technique based on relevance vector machines (RVM) and local model exchang
30#
發(fā)表于 2025-3-26 20:24:40 | 只看該作者
Christoph Lanzend?rfer,Resi Lanzend?rfer,Harald Schardelmann,Christian Subbenew and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well
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