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MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS | Learning the behavior of traffic flow
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 3639171608 ISBN 13: 9783639171600
Taschenbuch. Condition: Neu. MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS | Learning the behavior of traffic flow | Tomas Singliar | Taschenbuch | Englisch | VDM Verlag Dr. Müller | EAN 9783639171600 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
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Machine Learning Solutions For Transportation Networks: Learning The Behavior Of Traffic Flow
Language: English
Published by Vdm Verlag Dr. Müller, 2009
ISBN 10: 3639171608 ISBN 13: 9783639171600
US$ 142.10
US$ 13.46 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketPaperback. Condition: Brand New. 128 pages. 8.66x5.91x0.29 inches. In Stock.
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MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 3639171608 ISBN 13: 9783639171600
Kartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Singliar TomasTomas specializes in machine learning and anomaly detection,nespecially by means of graphical probability models. He obtainednhis PhD from University of Pittsburgh in 2008, authored papersnon inference in graphical mode.
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MACHINE LEARNING SOLUTIONS FOR TRANSPORTATION NETWORKS : Learning the behavior of traffic flow
Language: English
Published by VDM Verlag Dr. Müller, 2009
ISBN 10: 3639171608 ISBN 13: 9783639171600
Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This thesis brings a collection of novel models andmethods that result from a new look at practicalproblems in transportation through the prism of newlyavailable sensor data. From this data, we build a model of traffic flowinspired by macroscopic flow models. Unliketraditional such models, our model deals withuncertainty of measurement and unobservability ofcertain important quantities and incorporateson-the-fly observations more easily. Having apredictive distribution of traffic state enables theapplication of powerful decision-making machinery tothe traffic domain.Secondly, a new method for detecting accidents andother adverse events is described. Data collectedfrom highways enables us to bring supervised learningapproaches to incident detection. However, a majorhurdle to performance of supervised learners is thequality of data which contains systematic biasesvarying from site to site. We build a dynamicBayesian network framework that learns and rectifiesthese biases, leading to improved superviseddetector performance with little need for manuallytagged data. The realignment method applies generallyto virtually all forms of labeled sequential data.
