Skip to main content
SpringerLink
Log in
Book cover
  • Book
  • © 2008

Interval / Probabilistic Uncertainty and Non-classical Logics

Editors:

  1. Van-Nam Huynh
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Yoshiteru Nakamori
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  3. Hiroakira Ono
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  4. Jonathan Lawry
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  5. Vkladik Kreinovich
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  6. Hung T. Nguyen
    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • 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)

Sections

Buy it now

eBook USD 209.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

This is a preview of subscription content, log in via an institution to check for access.

Table of contents (28 chapters)

  1. Front Matter

    PDF

  2. Keynote Addresses

    1. Front Matter

      Pages 1-1
      PDF

  3. Statistics under Interval Uncertainty and Imprecise Probability

    1. Front Matter

      Pages 17-17
      PDF

    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

    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
      PDF

    4. Probabilistic Constraints for Inverse Problems

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

  5. Rough Sets and Belief Functions

    1. Front Matter

      Pages 161-161
      PDF

    2. Several Reducts in Dominance-Based Rough Set Approach

      • Masahiro Inuiguchi, Yukihiro Yoshioka
      Pages 163-175

    4. Uncertainty Reasoning in Rough Knowledge Discovery

      • Yaxin Bi, Xuhui Shen, Shengli Wu
      Pages 187-200
Back to top

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.
Back to top

Keywords

Back to top

Bibliographic Information

Back to top

Publish with us

Policies and ethics

Back to top
Access via your institution

Buy it now

eBook USD 209.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Other ways to access

Search

Navigation