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 presents thoughts and strategies for; integrating details from a number of resources and utilizing the complementarities of those detections to derive greatest information regarding the phenomenon being saw; reading and deriving the which means of those observations and predicting attainable effects of the saw nation of our surroundings; choosing the right plan of action; and controlling the activities. the following the point of interest is at the extra mature section of information fusion, specifically the detection and identification/classification of phenomena being saw and exploitation of the similar tools for Security-Related Civil technology and expertise (SST) purposes. it will be significant to; extend at the info fusion method pertinent to state of affairs tracking, Incident Detection, Alert and reaction administration; talk about a few comparable Cognitive Engineering and visualization matters; offer an perception into the architectures and methodologies for construction a knowledge fusion procedure; speak about fusion methods to photo exploitation with emphasis on defense purposes; talk about novel dispensed monitoring ways as an important step of scenario tracking and incident detection; and supply examples of actual events, within which facts fusion can improve incident detection, prevention and reaction strength. with the intention to supply a logical presentation of the information fusion fabric, first the final suggestions are highlighted (Fusion method, Human desktop Interactions and platforms and Architectures), last with a number of purposes (Data Fusion for Imagery, monitoring and Sensor Fusion and functions and possibilities for Fusion).IOS Press is a global technological know-how, technical and scientific writer of top quality books for teachers, scientists, and pros in all fields. many of the parts we submit in: -Biomedicine -Oncology -Artificial intelligence -Databases and knowledge platforms -Maritime engineering -Nanotechnology -Geoengineering -All elements of physics -E-governance -E-commerce -The wisdom economic climate -Urban reviews -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

So far, two main types of fusers are used. Integration (fusion) functions: for each pattern, all the classifiers contribute to the final decision. Integration assumes competitive classifiers. Selection functions: for each pattern, just one classifier, or a subset, is responsible for the final decision. Selection assumes complementary classifiers. Integration and selection can be “merged” for designing a hybrid fuser. Multiple functions may be necessary for non parallel architectures. So far, research on MCSs has focused on parallel architectures.

Roli, G. Giacinto, Design of Multiple Classifier Systems, in H. Bunke and A. ), Hybrid Methods in Pattern Recognition, World Scientific Publishing, 2002. V. Dasarathy, Decision Fusion, IEEE Computer Society Press, 1994. [27] Y. L. Marcialis, M. Pontil, P. Frasconi, F. Roli, Combining Flat and Structural Representations for Fingerprint Classification with Recursive Neural Networks and Support Vector Machine, Pattern Recognition, Vol. 36 (2), 2003, pp. 397–406. , Bagging Predictors, Machine Learning, 24, 123–140, 1996.

It is easy to see that you can avoid selecting the worst classifier by averaging over the individual 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.

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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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