Computational Discovery of Scientific Knowledge: by Sašo Džeroski, Pat Langley, Ljupčo Todorovski (auth.), Sašo

By Sašo Džeroski, Pat Langley, Ljupčo Todorovski (auth.), Sašo Džeroski, Ljupčo Todorovski (eds.)

Advances in know-how have enabled the gathering of information from medical observations, simulations, and experiments at an ever-increasing velocity. For the scientist and engineer to learn from those more desirable information gathering services, it really is changing into transparent that semi-automated facts research concepts has to be utilized to discover the necessary info within the info. Computational clinical discovery tools can be utilized to this finish: they specialise in utilizing computational easy methods to automate medical actions, similar to discovering legislation from observational information. unlike mining clinical info, which specializes in development hugely predictive versions, computational clinical discovery places a powerful emphasis on studying wisdom represented in formalisms utilized by scientists and engineers, akin to numeric equations and response pathways.

This cutting-edge survey offers an creation to computational ways to the invention of medical wisdom and offers an summary of modern advances during this sector, together with concepts and functions in environmental and lifestyles sciences. The 15 articles provided are partially encouraged through the contributions of the foreign Symposium on Computational Discovery of Communicable wisdom, held in Stanford, CA, united states in March 2001. extra consultant assurance of modern learn in computational medical discovery is accomplished via an important variety of extra invited contributions.

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Extra info for Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences

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Special Volume on Qualitative Reasoning About Physical Systems II, vol. 51. Elsevier Science, Amsterdam (1991) 42 R. Stolle and E. : Discovering dynamics: From inductive logic programming to machine discovery. : Generalized physical networks for automated model building. In: Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence, pp. 1047–1053. : Reasoning about input-output modeling of dynamical systems. In: Proceedings of the Third International Symposium on Intelligent Data Analysis, pp.

Integrated multimodal reasoning for modeling of physical systems. In: Doctoral dissertation, University of Colorado at Boulder. : Multimodal reasoning for automatic model construction. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 181– 188. : CONSTRAINTS—a language for expressing almost hierarchical descriptions. : Declarative bias in equation discovery. In: Proceedings of the Fourteenth International Conference on Machine Learning, pp. 376–384. : Discovering admissible model equations from observed data based on scale-types and identity constraints.

Stolle and E. Bradley representations and their integration with numerical information and techniques falls largely into the category of qualitative physics. The project in this branch of the literature that is most closely related to Pret is the QR-based viscoelastic system modeling tool developed by Capelo et al. (1998), which also builds ODE models from time-series data. 15 Pret takes a strict engineering approach to the questions of accuracy and parsimony. Its goal is to find an ODE system that serves as a useful model of the target system in the context of engineering tasks, such as controller design.

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