16,139 research outputs found
Formalism and judgement in assurance cases
This position paper deals with the tension between the desire for sound and auditable assurance cases and the current ubiquitous reliance on expert judgement. I believe that the use of expert judgement, though inevitable, needs to be much more cautious and disciplined than it usually is. The idea of assurance “cases ” owes its appeal to an awareness that all too often critical decisions are made in ways that are difficult to justify or even to explain, leaving the doubt (for the decision makers as well as other interested parties) that the decision may be unsound. By building a well-structured “case ” we would wish to allow proper scrutiny of the evidence and assumptions used, and of the arguments that link them to support a decision. A
Representation of Functional Data in Neural Networks
Functional Data Analysis (FDA) is an extension of traditional data analysis
to functional data, for example spectra, temporal series, spatio-temporal
images, gesture recognition data, etc. Functional data are rarely known in
practice; usually a regular or irregular sampling is known. For this reason,
some processing is needed in order to benefit from the smooth character of
functional data in the analysis methods. This paper shows how to extend the
Radial-Basis Function Networks (RBFN) and Multi-Layer Perceptron (MLP) models
to functional data inputs, in particular when the latter are known through
lists of input-output pairs. Various possibilities for functional processing
are discussed, including the projection on smooth bases, Functional Principal
Component Analysis, functional centering and reduction, and the use of
differential operators. It is shown how to incorporate these functional
processing into the RBFN and MLP models. The functional approach is illustrated
on a benchmark of spectrometric data analysis.Comment: Also available online from:
http://www.sciencedirect.com/science/journal/0925231
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