20 resultados para Optimal Testing


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41 p.

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36 p.

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In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.

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This paper presents a theoretical and experimental study of multidirectional steel fibers reinforced concrete slabs (SFRC). The study is based on a real building application using SFRC flag slabs. For the evaluation of the slabs bearing capacity, plastic calculations are performed both at section and structure levels. The section analysis uses the perfect plastic stress-strain diagram, with reference to the values of the strength characteristics of SFRC based on previous jobs that used similar fibers and dosages. In the structure analysis the plastic yield lines method has been used. This method relates the section last bearing moment and the plastic collapse load. The experimental campaign has consisted of the testing of six 2 m. diameter circular shaped slabs prototypes, and has allowed to verify the reference resistance used in the calculations.