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

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

ISBN-10: 354073919X

ISBN-13: 9783540739197

Advances in expertise have enabled the gathering of information from medical observations, simulations, and experiments at an ever-increasing velocity. For the scientist and engineer to profit from those improved facts accumulating features, it really is changing into transparent that semi-automated info research suggestions needs to be utilized to discover the worthy details within the info. Computational medical discovery equipment can be utilized to this finish: they specialise in employing computational how to automate clinical actions, equivalent to discovering legislation from observational facts. unlike mining medical information, which specializes in development hugely predictive types, computational clinical discovery places a robust emphasis on learning wisdom represented in formalisms utilized by scientists and engineers, similar to numeric equations and response pathways.

This state of the art survey offers an advent to computational techniques to the invention of clinical wisdom and provides an summary of contemporary advances during this zone, together with options and purposes in environmental and lifestyles sciences. The 15 articles awarded are in part encouraged by way of the contributions of the overseas Symposium on Computational Discovery of Communicable wisdom, held in Stanford, CA, united states in March 2001. extra consultant assurance of modern learn in computational clinical discovery is accomplished through an important variety of extra invited contributions.

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

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Pret automates both phases of this process. In linear physical systems, structural identification and parameter estimation are fairly well understood. The difficulties—and the subtleties employed by practitioners—arise where noisy or incomplete data are involved, or where 26 R. Stolle and E. Bradley efficiency is an issue. See (Juang, 1994; Ljung, 1987) for some examples. In nonlinear systems, however, both procedures are vastly more difficult—the type of material that is covered only in the last few pages of standard textbooks.

The key to making gray-box modeling of nonlinear dynamic systems practical in an automated modeling tool is a flexible knowledge representation scheme that adapts to the problem at hand. Domain-dependent knowledge can drastically reduce the search-space size, but its applicability is fundamentally limited. The challenge in balancing these influences is to be able to determine, at every point in the reasoning procedure, what knowledge is applicable and useful. The goal of the work described in this chapter was to develop a knowledge representation and reasoning (KRR) framework that supports automated modeling in a range of gray shades that is useful to practicing engineers.

It is, of course, possible to use Pret as a scientific discovery tool by supplying several sets of observations to it in separate runs and then unifying the results by hand. Pret can also be used to solve the kinds of cross-validation problems that arise in the machine learning literature: one would simply use it to perform several individual validation runs and then interpret the results. , this volume) and (Saito & Langley, this volume), Pret makes direct contact with the applicable domain theory, and leverages that information in the model-building process.

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Computational Discovery of Scientific Knowledge: Introduction, Techniques, and Applications in Environmental and Life Sciences by Sašo Džeroski, Pat Langley, Ljupčo Todorovski (auth.), Sašo Džeroski, Ljupčo Todorovski (eds.)


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