Interval / Probabilistic Uncertainty and Non-classical Logics
Published on 19. February 2008
Book
Paperback/Softback
XVIII, 376 pages
978-3-540-77663-5 (ISBN)
Description
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.
More details
Series
Edition
2008 ed.
Language
English
Place of publication
Berlin
Germany
Publishing group
Springer Berlin
Target group
Professional and scholarly
Research
Illustrations
79 s/w Abbildungen
XVIII, 376 p. 79 illus.
Dimensions
Height: 23.5 cm
Width: 15.5 cm
ISBN-13
978-3-540-77663-5 (9783540776635)
DOI
10.1007/978-3-540-77664-2
Schweitzer Classification
Other editions
Additional editions
Van-Nam Huynh | Yoshiteru Nakamori | Hiroakira Ono
Interval / Probabilistic Uncertainty and Non-classical Logics
E-Book
01/2008
1st Edition
Springer
€203.29
Available for download
Persons
Content
Keynote Addresses.- An Algebraic Approach to Substructural Logics - An Overview.- On Modeling of Uncertainty Measures and Observed Processes.- Statistics under Interval Uncertainty and Imprecise Probability.- Fast Algorithms for Computing Statistics under Interval Uncertainty: An Overview.- Trade-Off between Sample Size and Accuracy: Case of Static Measurements under Interval Uncertainty.- Trade-Off between Sample Size and Accuracy: Case of Dynamic Measurements under Interval Uncertainty.- Estimating Quality of Support Vector Machines Learning under Probabilistic and Interval Uncertainty: Algorithms and Computational Complexity.- Imprecise Probability as an Approach to Improved Dependability in High-Level Information Fusion.- Uncertainty Modelling and Reasoning in Knowledge-Based Systems.- Label Semantics as a Framework for Granular Modelling.- Approximating Reasoning for Fuzzy-Based Information Retrieval.- Probabilistic Constraints for Inverse Problems.- The Evidential Reasoning Approach for Multi-attribute Decision Analysis under Both Fuzzy and Interval Uncertainty.- Modelling and Computing with Imprecise and Uncertain Properties in Object Bases.- Rough Sets and Belief Functions.- Several Reducts in Dominance-Based Rough Set Approach.- Topologies of Approximation Spaces of Rough Set Theory.- Uncertainty Reasoning in Rough Knowledge Discovery.- Semantics of the Relative Belief of Singletons.- A Lattice-Theoretic Interpretation of Independence of Frames.- Non-classical Logics.- Completions of Ordered Algebraic Structures: A Survey.- The Algebra of Truth Values of Type-2 Fuzzy Sets: A Survey.- Some Properties of Logic Functions over Multi-interval Truth Values.- Possible Semantics for a Common Framework of Probabilistic Logics.- A Unified Formulation of Deduction,Induction and Abduction Using Granularity Based on VPRS Models and Measure-Based Semantics for Modal Logics.- Information from Inconsistent Knowledge: A Probability Logic Approach.- Fuzziness and Uncertainty Analysis in Applications.- Personalized Recommendation for Traditional Crafts Using Fuzzy Correspondence Analysis with Kansei Data and OWA Operator.- A Probability-Based Approach to Consumer Oriented Evaluation of Traditional Craft Items Using Kansai Data.- Using Interval Function Approximation to Estimate Uncertainty.- Interval Forecasting of Crude Oil Price.- Automatic Classification for Decision Making of the Severeness of the Acute Radiation Syndrome.