Search results for "VDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412"

showing 4 items of 4 documents

A Spatio-temporal Probabilistic Model of Hazard and Crowd Dynamics in Disasters for Evacuation Planning

2013

Published version of a chapter in the book: Recent Trends in Applied Artificial Intelligence. Also available from the publisher at: http://dx.doi.org/10.1007/978-3-642-38577-3_7 Managing the uncertainties that arise in disasters – such as ship fire – can be extremely challenging. Previous work has typically focused either on modeling crowd behavior or hazard dynamics, targeting fully known environments. However, when a disaster strikes, uncertainty about the nature, extent and further development of the hazard is the rule rather than the exception. Additionally, crowd and hazard dynamics are both intertwined and uncertain, making evacuation planning extremely difficult. To address this chal…

Hazard (logic)Crowd dynamicsOperations researchVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412Computer scienceHazard Modeling02 engineering and technologyCrowd ModelingTime step11. Sustainability0202 electrical engineering electronic engineering information engineeringCrowd psychologyDynamic Bayesian networkbusiness.industryEvacuation Planning020207 software engineeringStatistical modelCrowd modelingAnt Based Colony OptimizationCrowd evacuation13. Climate action[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]020201 artificial intelligence & image processingArtificial intelligenceDynamic Bayesian Networksbusiness
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The fundamental theory of optimal "Anti-Bayesian" parametric pattern classification using order statistics criteria

2013

Author's version of an article in the journal: Pattern Recognition. Also available from the publisher at: http://dx.doi.org/10.1016/j.patcog.2012.07.004 The gold standard for a classifier is the condition of optimality attained by the Bayesian classifier. Within a Bayesian paradigm, if we are allowed to compare the testing sample with only a single point in the feature space from each class, the optimal Bayesian strategy would be to achieve this based on the (Mahalanobis) distance from the corresponding means. The reader should observe that, in this context, the mean, in one sense, is the most central point in the respective distribution. In this paper, we shall show that we can obtain opti…

Mahalanobis distanceVDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412Feature vectorOrder statisticBayesian probabilityclassification by moments of order statistics020206 networking & telecommunicationsVDP::Technology: 500::Information and communication technology: 55002 engineering and technologyprototype reduction schemesNaive Bayes classifierBayes' theoremExponential familypattern classificationorder statisticsArtificial IntelligenceSignal Processing0202 electrical engineering electronic engineering information engineering020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionAlgorithmSoftwarereduction of training patternsMathematicsParametric statistics
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Statistikk og sannsynlighetsregning : en skisse av det statistiske miljø i Norge fra ca. 1850 og emnenes plass i den norske skolen i nyere tid

2011

Masteroppgave i matematikkdidaktikk- Universitetet i Agder 2011 The topic of this thesis is statistics and probability theory in Norway. The research question is: Which position has statistics and probability theory had in the Norwegian school, and how has the statistics environment developed in Norway from the middle of the 18th century? First, I will provide a historical overview of how the statistics environment has developed in Norway from around 1850 and until 1960s-1970s. Statistisk sentralbyrå (1876), Den norske aktuarforening (1904), Norsk matematisk forening (1918) and Norsk statistisk forening (1936) will be central. I will also provide an overview of how the education in mathemat…

VDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412MA 500
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On Optimizing Locally Linear Nearest Neighbour Reconstructions Using Prototype Reduction Schemes

2010

Published version of an article from the Book: AI 2010: Advances in Artificial Intelligence, Spinger. Also available on Springerlink: http://dx.doi.org/10.1007/978-3-642-17432-2_16 This paper concerns the use of Prototype Reduction Schemes (PRS) to optimize the computations involved in typical k-Nearest Neighbor (k-NN) rules. These rules have been successfully used for decades in statistical Pattern Recognition (PR) applications, and have numerous applications because of their known error bounds. For a given data point of unknown identity, the k-NN possesses the phenomenon that it combines the information about the samples from a priori target classes (values) of selected neighbors to, for …

VDP::Mathematics and natural science: 400::Mathematics: 410::Statistics: 412VDP::Mathematics and natural science: 400::Information and communication science: 420::Algorithms and computability theory: 422
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