Download e-book for kindle: Data Fusion for Situation Monitoring, Incident Detection, by E. Shahbazian, G. Rogova, P. Valin

By E. Shahbazian, G. Rogova, P. Valin

ISBN-10: 1429405791

ISBN-13: 9781429405799

ISBN-10: 1586035363

ISBN-13: 9781586035365

Info Fusion is a truly large interdisciplinary know-how area. It offers thoughts and strategies for; integrating info from a number of assets and utilizing the complementarities of those detections to derive greatest information regarding the phenomenon being saw; reading and deriving the that means of those observations and predicting attainable outcomes of the saw nation of our surroundings; selecting the right plan of action; and controlling the activities. right here the focal point is at the extra mature part of knowledge fusion, particularly the detection and identification/classification of phenomena being saw and exploitation of the similar equipment for Security-Related Civil technology and know-how (SST) functions. it is vital to; extend at the info fusion technique pertinent to state of affairs tracking, Incident Detection, Alert and reaction administration; talk about a few similar Cognitive Engineering and visualization matters; offer an perception into the architectures and methodologies for development an information fusion method; talk about fusion methods to photograph exploitation with emphasis on protection purposes; talk about novel disbursed monitoring methods as an important step of scenario tracking and incident detection; and supply examples of actual events, within which facts fusion can increase incident detection, prevention and reaction power. that allows you to supply a logical presentation of the knowledge fusion fabric, first the overall techniques are highlighted (Fusion method, Human laptop Interactions and platforms and Architectures), final with numerous functions (Data Fusion for Imagery, monitoring and Sensor Fusion and purposes and possibilities for Fusion).IOS Press is a world technological know-how, technical and clinical writer of top of the range books for teachers, scientists, and pros in all fields. a number of the parts we submit in: -Biomedicine -Oncology -Artificial intelligence -Databases and knowledge structures -Maritime engineering -Nanotechnology -Geoengineering -All features of physics -E-governance -E-commerce -The wisdom economic system -Urban stories -Arms regulate -Understanding and responding to terrorism -Medical informatics -Computer Sciences

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Extra resources for Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management (NATO Science Series. 3: Computer and Systems Sciences)

Example text

Windeatt and F. , LNCS 2709, pp. 55–64. , Stacked generalisation, Neural Networks 5, 241–259, 1992. [38] G. Giacinto, and F. Roli, Ensembles of Neural Networks for Soft Classification of Remote Sensing Images, Proc. of the European Symposium on Intelligent Techniques, March 20–21, 1997, Bari, Italy, 1997. Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management E. Shahbazian et al. ) IOS Press, 2005 © 2005 IOS Press. All rights reserved. 35 Uncertainty Management for Intelligence Analysis Ronald R.

Trained rules. 1. Abstract-Level Fusion Rules Majority voting: let us consider the N abstract (“crisp”) classifiers outputs S(1),…,S(N) associated to the pattern x. Majority fusion rule: class label c i is assigned to the pattern x if ci is the most frequent label in the crisp classifiers outputs. Usually N is odd. The frequency of the winner class must be at least N/2. 5, then it can be shown that the error of the majority voting rule is monotonically decreasing in N [34]. 30 F. Roli / A Gentle Introduction to Fusion of Multiple Pattern Classifiers Clearly, the performances of the majority vote quickly decrease for dependent classifiers.

I call this the “worst” case motivation for the use of multiple pattern classifier fusion, as it points out that fusion can protect you against the selection of the worst classifier. Besides avoiding the selection of the worst classifier, under particular hypotheses, fusion of multiple classifiers can improve the performance of the best individual classifier and, in some special cases, provides the optimal Bayes classifier. This is possible if individual classifiers make “complementary” errors.

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Data Fusion for Situation Monitoring, Incident Detection, Alert and Response Management (NATO Science Series. 3: Computer and Systems Sciences) by E. Shahbazian, G. Rogova, P. Valin

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