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Aggregation and Fusion of Imperfect Information
Aggregation and Fusion of Imperfect Information
Knygos.lt klubas Knygos.lt nariams
167,15 €
-30%
Įprastai
238,79 €
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This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and p…
  • Leidėjas:
  • Metai: 2013
  • Puslapiai: 278
  • ISBN-10: 3662110733
  • ISBN-13: 9783662110737
  • Formatas: 15.6 x 23.4 x 1.5 cm, minkšti viršeliai
  • Kalba: Anglų

Aggregation and Fusion of Imperfect Information (el. knyga) (skaityta knyga) | knygos.lt

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This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and possibility theory. The latter is more developed because it allows to manage both imprecise and uncertain knowledge. Applications to decision-making, image processing, control and classification are described.

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  • Leidėjas:
  • Metai: 2013
  • Puslapiai: 278
  • ISBN-10: 3662110733
  • ISBN-13: 9783662110737
  • Formatas: 15.6 x 23.4 x 1.5 cm, minkšti viršeliai
  • Kalba: Anglų

This book presents the main tools for aggregation of information given by several members of a group or expressed in multiple criteria, and for fusion of data provided by several sources. It focuses on the case where the availability knowledge is imperfect, which means that uncertainty and/or imprecision must be taken into account. The book contains both theoretical and applied studies of aggregation and fusion methods in the main frameworks: probability theory, evidence theory, fuzzy set and possibility theory. The latter is more developed because it allows to manage both imprecise and uncertain knowledge. Applications to decision-making, image processing, control and classification are described.

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