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

性满足

Logic and the Automatic Acquisition of Scientific Knowledge: An Application to Functional Genomics 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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Ischemia

Book 2007 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 app

faculty

Computational Discovery of Scientific Knowledgetational scientific discovery and discuss the lessons learned, especially in relation to work in data mining that has recently received substantial attention. Finally, we discuss the contents of the book and how it fits in the overall framework of computational scientific discovery.

Pander

GULP

Drug Discovery as an Example of Literature-Based Discovery, the user, typically a biomedical scientist, can efficiently filter out irrelevant information. This chapter provides an algorithmic description of the system and presents a potential drug discovery. We conclude by discussing the current and future status of literature-based discovery in the biomedical research domain.

PANEL

0302-9743 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 fo

nerve-sparing

neue betriebswirtschaftliche forschung (nbf)aximize communicability, and then describe two visualization techniques that we developed to aid in understanding the model by exploiting the spatial nature of the data. We also report how evaluating the learned models across time let us discover an error in the data.

沉积物

https://doi.org/10.1007/978-3-322-90023-4dge in pure mathematics. We discuss to what extent the output from certain programs can be considered a discovery in pure mathematics. This enables us to assess the state of the art with respect to Newell and Simon’s prediction that a computer would discover and prove an important mathematical theorem.

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