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Source : Archipel

Building Quality Estimation models with Fuzzy Threshold Values

Sahraoui, Houari A. et al. (2001)

This work presents an approach to circumvent one of the major problems with techniques to build and apply software quality estimation models, namely the use of precise metric thresholds values. We used a fuzzy logic based approach to investigate the stability of a reusable class library interface, ...

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Source : DANS

Structured data entry for narrative data in a broad specialty: patient history and physical examination in pediatrics.

Bleeker, Sacha et al. (13 juil. 2006)

textabstractBACKGROUND: Whereas an electronic medical record (EMR) system can partly address the limitations, of paper-based documentation, such as fragmentation of patient data, physical paper records missing and poor legibility, structured data entry (SDE, i.e. data entry based on selection of ...

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Source : DANS

Mapping and monitoring forest remnants: A multi-scale analysis of spatio-temporal data

Carvalho, de, L.M.T. (2001)

KEYWORDS : Landsat, time series, machine learning, semideciduous Atlantic forest, Brazil, wavelet transforms, classification, change detectionForests play a major role in important global matters such as carbon cycle, climate change, and biodiversity. Besides, forests also influence soil and water ...

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Source : DANS

On Fuzzy Concepts

Hoede, C. et al. (déc. 2006)

In this paper we try to combine two approaches. One is the theory of knowledge graphs in which concepts are represented by graphs. The other is the axiomatic theory of fuzzy sets (AFS). The discussion will focus on the idea of fuzzy concept. It will be argued that the fuzziness of a concept in ...

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Source : HAL

Evolutionary Algorithm for Decision Tree Induction

Jankowski, Dariusz et al. (5 nov. 2014)

Part 2: Algorithms International audience Decision trees are among the most popular classification algorithms due to their knowledge representation in form of decision rules which are easy for interpretation and analysis. Nonetheless, a majority of decision trees training algorithms base on greedy ...

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Source : UPV

Particulate matter emitted from poultry and pig houses: Source identification and quantification

Cambra López, María et al. (avr. 2011)

Copyright 2011 - American Society of Agricultural and Biological Engineers ISSN 2151-0032 [EN] There is need to identify and quantify the contribution of different sources to airborne particulate matter (PM) emissions from animal houses. To this end, we compared the chemical and morphological ...

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Source : Persée

La mémoire des joueurs d'échecs

Lories, G. (1984)

Résumé Cet article considère les moyens par lesquels la perception et la mémoire humaine permettent de jouer aux échecs en expert. On parcourt les recherches récentes et on examine les possibilités ainsi soulevées, à la lumière de certains travaux d'intelligence artificielle. Les ...

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Source : DANS

The Higgs boson in the H → ZZ(*) → 4l decay channel with the ATLAS detector at the LHC

Salvucci, A. (2014)

The Standard Model of particle physics is a very successful theory that describes the known matter in terms of its elementary constituents and their interactions. It describes and uni es three out of the four fundamental interactions: the electromagnetic interaction, responsible for the interactions ...

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Source : DANS

Selection of particle characteristics to distinguish amongst potential sources of particulate matter in poultry and pigs.

Cambra-Lopez, M. et al. (2011)

The knowledge on the contribution of individual sources to particulate matter (PM) in different size fractions is essential to improve PM reduction from livestock houses. We investigated which input data (particle chemical, morphological or combined characteristics) were best to distinguish amongst ...

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Source : DANS

On the use of simple classifiers for the initialisation of one-hidden-layer neural nets

Bioch, Cor et al. (1 janv. 1995)

textabstractIn this report we discuss the use of two simple classifiers to initialise the input-to-hidden layer of a one-hidden-layer neural network. These classifiers divide the input space in convex regions that can be represented by membership functions. These functions are then used to determine ...

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