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Titlebook: Computational Discovery of Scientific Knowledge; Introduction, Techni Sašo Džeroski,Ljupčo Todorovski Book 2007 Springer-Verlag Berlin Heid

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书目名称Computational Discovery of Scientific Knowledge
副标题Introduction, Techni
编辑Sašo Džeroski,Ljupčo Todorovski
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Discovery of Scientific Knowledge; Introduction, Techni Sašo Džeroski,Ljupčo Todorovski Book 2007 Springer-Verlag Berlin Heid
描述.Advances in technology have enabled the collection of data from scientific observations, simulations, and experiments at an ever-increasing pace. For the scientist and engineer to benefit from these enhanced data collecting capabilities, it is becoming clear that semi-automated data analysis techniques must be applied to find the useful information in the data. Computational scientific discovery methods can be used to this end: they focus on applying computational methods to automate scientific activities, such as finding laws from observational data. In contrast to mining scientific data, which focuses on building highly predictive models, computational scientific discovery puts a strong emphasis on discovering knowledge represented in formalisms used by scientists and engineers, such as numeric equations and reaction pathways. ..This state-of-the-art survey provides an introduction to computational approaches to the discovery of scientific knowledge and gives an overview of recent advances in this area, including techniques and applications in environmental and life sciences. The 15 articles presented are partly inspired by the contributions of the International Symposium on Com
出版日期Book 2007
关键词DOM; Simulation; automated modeling; bioinformatics; biomedical applications; constraint-based mining; dat
版次1
doihttps://doi.org/10.1007/978-3-540-73920-3
isbn_softcover978-3-540-73919-7
isbn_ebook978-3-540-73920-3Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2007
The information of publication is updating

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https://doi.org/10.1007/978-3-322-87969-1 describe an example of using ILP to analyse a large and complex bioinformatic database that has produced unexpected and interesting scientific results in functional genomics. We then point a possible way forward to integrating machine learning with scientific databases to form intelligent databases.
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Communicable Knowledge in Automated System Identificationask-specific modeling constraints to the program. . inputs, its outputs, and its internal knowledge base are instances of communicable knowledge—knowledge that is represented in a form that is meaningful to the domain experts that are the intended users of the program.
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