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Interval / Probabilistic Uncertainty and Non-classical Logics

  • Book
  • © 2008

Overview

Editors:
  1. Van-Nam Huynh
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  2. Yoshiteru Nakamori
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  3. Hiroakira Ono
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  4. Jonathan Lawry
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  5. Vkladik Kreinovich
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  6. Hung T. Nguyen
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  • Proceedings of the International Workshop on Interval/Probabilistic Uncertainty and Non Classical Logics (UncLog'08), Ishikawa, Japan, March 25-28, 2008
  • Recent developments in Uncertainty and Non-classical Logics

Part of the book series: Advances in Intelligent and Soft Computing (AINSC, volume 46)

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Table of contents (28 chapters)

  1. Front Matter

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  2. Keynote Addresses

    1. Front Matter

      Pages 1-1
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    2. An Algebraic Approach to Substructural Logics – An Overview

      • Hiroakira Ono
      Pages 3-4

    3. On Modeling of Uncertainty Measures and Observed Processes

      • Hung T. Nguyen
      Pages 5-15

  3. Statistics under Interval Uncertainty and Imprecise Probability

    1. Front Matter

      Pages 17-17
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    2. Fast Algorithms for Computing Statistics under Interval Uncertainty: An Overview

      • Vladik Kreinovich, Gang Xiang
      Pages 19-31

    3. Trade-Off between Sample Size and Accuracy: Case of Static Measurements under Interval Uncertainty

      • Hung T. Nguyen, Vladik Kreinovich
      Pages 32-44

    4. Trade-Off between Sample Size and Accuracy: Case of Dynamic Measurements under Interval Uncertainty

      • Hung T. Nguyen, Olga Kosheleva, Vladik Kreinovich, Scott Ferson
      Pages 45-56

    5. Estimating Quality of Support Vector Machines Learning under Probabilistic and Interval Uncertainty: Algorithms and Computational Complexity

      • Canh Hao Nguyen, Tu Bao Ho, Vladik Kreinovich
      Pages 57-69

    6. Imprecise Probability as an Approach to Improved Dependability in High-Level Information Fusion

      • Alexander Karlsson, Ronnie Johansson, Sten F. Andler
      Pages 70-84

  4. Uncertainty Modelling and Reasoning in Knowledge-Based Systems

    1. Front Matter

      Pages 85-85
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    2. Label Semantics as a Framework for Granular Modelling

      • Jonathan Lawry
      Pages 87-102

    3. Approximating Reasoning for Fuzzy-Based Information Retrieval

      • Tho T. Quan, Tru H. Cao
      Pages 103-114

    4. Probabilistic Constraints for Inverse Problems

      • Elsa Carvalho, Jorge Cruz, Pedro Barahona
      Pages 115-128

    5. The Evidential Reasoning Approach for Multi-attribute Decision Analysis under Both Fuzzy and Interval Uncertainty

      • Min Guo, Jian-Bo Yang, Kwai-Sang Chin, Hongwei Wang
      Pages 129-140

    6. Modelling and Computing with Imprecise and Uncertain Properties in Object Bases

      • Tru H. Cao, Hoa Nguyen, Ma Nam
      Pages 141-159

  5. Rough Sets and Belief Functions

    1. Front Matter

      Pages 161-161
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    2. Several Reducts in Dominance-Based Rough Set Approach

      • Masahiro Inuiguchi, Yukihiro Yoshioka
      Pages 163-175

    3. Topologies of Approximation Spaces of Rough Set Theory

      • Milan Vlach
      Pages 176-186

    4. Uncertainty Reasoning in Rough Knowledge Discovery

      • Yaxin Bi, Xuhui Shen, Shengli Wu
      Pages 187-200
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Keywords

About this book

Large-scale data processing is important. Most successful applications of m- ern science and engineering, from discovering the human genome to predicting weather to controlling space missions, involve processing large amounts of data and large knowledge bases. The corresponding large-scale data and knowledge processing requires intensive use of computers. Computers are based on processing exact data values and truth values from the traditional 2-value logic. The ability of computers to perform fast data and knowledgeprocessingisbasedonthehardwaresupportforsuper-fastelementary computer operations, such as performing arithmetic operations with (exactly known) numbers and performing logical operations with binary (“true”-“false”) logical values. In practice, we need to go beyond exact data values and truth values from the traditional 2-value logic. In practical applications, we need to go beyond such operations. Input is only known with uncertainty. Let us ?rst illustrate this need on the example of operations with numbers. Hardware-supported computer operations (implicitly) assume that we know the exact values of the input quantities. In reality, the input data usually comes from measurements. Measurements are never 100% accurate. Due to such factors as imperfection of measurement - struments and impossibility to reduce noise level to 0, the measured value x of each input quantity is, in general, di?erent from the (unknown) actual value x of this quantity. It is therefore necessary to ?nd out how this input uncertainty def ?x = x ?x = 0 a?ects the results of data processing.

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