Computing Statistics under Interval and Fuzzy Uncertainty

14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 Computing Variance under Interval Uncertainty: An Example of an ... 221 225 Part III Towards Computing Statistics under Interval and Fuzzy Uncertainty: Gauging the Quality of the Input Data ...

Author: Hung T. Nguyen

Publisher: Springer Science & Business Media

ISBN: 9783642249044

Category: Mathematics

Page: 412

View: 636

In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area. Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 + 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy. This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to information technology (maintaining privacy), to computer engineering (design of computer chips), and to data processing in geosciences, radar imaging, and structural mechanics.

... Computing Statistics under Interval and Fuzzy Uncertainty. SCI, vol. 393. Springer, Heidelberg (2012) 7. Papadimitriou, C.H.: Computational Complexity. Addison Wesley, San Diego (1994) 8. Sheskin, D.J.: Handbook of Parametric and ...

Author: Mo Jamshidi

Publisher: Springer

ISBN: 9783319036748

Category: Technology & Engineering

Page: 468

View: 596

This book is the proceedings of the 3rd World Conference on Soft Computing (WCSC), which was held in San Antonio, TX, USA, on December 16-18, 2013. It presents start-of-the-art theory and applications of soft computing together with an in-depth discussion of current and future challenges in the field, providing readers with a 360 degree view on soft computing. Topics range from fuzzy sets, to fuzzy logic, fuzzy mathematics, neuro-fuzzy systems, fuzzy control, decision making in fuzzy environments, image processing and many more. The book is dedicated to Lotfi A. Zadeh, a renowned specialist in signal analysis and control systems research who proposed the idea of fuzzy sets, in which an element may have a partial membership, in the early 1960s, followed by the idea of fuzzy logic, in which a statement can be true only to a certain degree, with degrees described by numbers in the interval [0,1]. The performance of fuzzy systems can often be improved with the help of optimization techniques, e.g. evolutionary computation, and by endowing the corresponding system with the ability to learn, e.g. by combining fuzzy systems with neural networks. The resulting “consortium” of fuzzy, evolutionary, and neural techniques is known as soft computing and is the main focus of this book.

Foundations of Fuzzy Logic and Soft Computing

Estimating statistics under fuzzy uncertainty: precise formulation of the problem. ... characteristic under fuzzy uncertainty can be reduced to the problem of computing this characteristic under interval uncertainty; see, e.g., [2].

Author: Patricia Melin

Publisher: Springer Science & Business Media

ISBN: 9783540729174

Page: 836

View: 630

This book comprises a selection of papers from IFSA 2007 on new methods and theories that contribute to the foundations of fuzzy logic and soft computing. Coverage includes the application of fuzzy logic and soft computing in flexible querying, philosophical and human-scientific aspects of soft computing, search engine and information processing and retrieval, as well as intelligent agents and knowledge ant colony.

Recent Advancements in Multi View Data Analytics

Sheskin, D.J.: Handbook of Parametric and Non-Parametric Statistical Procedures. Chapman & Hall/CRC, London (2011) 4. ... Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.: Computing Statistics Under Interval and Fuzzy Uncertainty.

Author: Witold Pedrycz

Publisher: Springer Nature

ISBN: 9783030952396

Category: Big data

Page: 346

View: 903

This book provides timely studies on multi-view facets of data analytics by covering recent trends in processing and reasoning about data originating from an array of local sources. A multi-view nature of data analytics is encountered when working with a variety of real-world scenarios including clustering, consensus building in decision processes, computer vision, knowledge representation, big data, data streaming, among others. The chapters demonstrate recent pursuits in the methodology, theory, advanced algorithms, and applications of multi-view data analytics and bring new perspectives of data interpretation. The timely book will appeal to a broad readership including both researchers and practitioners interested in gaining exposure to the rapidly growing trend of multi-view data analytics and intelligent systems.

Uncertainty and Imprecision in Decision Making and Decision Support New Challenges Solutions and Perspectives

Selected Papers from BOS-2018, held on September 24-26, 2018, and IWIFSGN-2018, held on September 27-28, 2018 in ... (1959) Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.: Computing Statistics Under Interval and Fuzzy Uncertainty.

Author: Krassimir T. Atanassov

Publisher: Springer Nature

ISBN: 9783030470241

Category: Technology & Engineering

Page: 440

View: 688

This book gathers selected papers from two important conferences held on October 24–28, 2018, in Warsaw, Poland: theFifteenth National Conference of Operational and Systems Research, BOS-2018, one of the leading conferences in the field of operational and systems research not only in Poland but also at the European level; andthe Seventeenth International Workshop on Intuitionistic Fuzzy Sets and General Nets, IWIFSGN-2018, one of thepremiere conferences on fuzzy logic. The papers presented here constitute a fair and comprehensive representation of the topics covered by both BOS-2018 and IWIFSGN-2018, includingextensions of the traditional fuzzy sets, in particular on the intuitionistic fuzzy sets, as well as other topics in uncertainty and imprecision modeling, the Generalized Nets (GNs), a powerful extension of the traditional Petri net paradigm, and InterCriteria Analysis, a new method for feature selection and analyses in multicriteria and multi-attribute decision-making problems. The Workshop was dedicated to the memory of Professor Beloslav Riečan (1936–2018), a regular participant at the IWIFSGN workshops.

Bounded Rationality in Decision Making Under Uncertainty Towards Optimal Granularity

In: Proceedings of the IEEE International Conference on Fuzzy Systems FUZZ-IEEE'2015, Istanbul, Turkey, 1–5 August 2015 Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.: Computing Statistics Under Interval and Fuzzy Uncertainty.

Author: Joe Lorkowski

Publisher: Springer

ISBN: 9783319622149

Category: Technology & Engineering

Page: 164

View: 915

This book addresses an intriguing question: are our decisions rational? It explains seemingly irrational human decision-making behavior by taking into account our limited ability to process information. It also shows with several examples that optimization under granularity restriction leads to observed human decision-making. Drawing on the Nobel-prize-winning studies by Kahneman and Tversky, researchers have found many examples of seemingly irrational decisions: e.g., we overestimate the probability of rare events. Our explanation is that since human abilities to process information are limited, we operate not with the exact values of relevant quantities, but with “granules” that contain these values. We show that optimization under such granularity indeed leads to observed human behavior. In particular, for the first time, we explain the mysterious empirical dependence of betting odds on actual probabilities. This book can be recommended to all students interested in human decision-making, to researchers whose work involves human decisions, and to practitioners who design and employ systems involving human decision-making —so that they can better utilize our ability to make decisions under uncertainty.

Statistical and Fuzzy Approaches to Data Processing with Applications to Econometrics and Other Areas

3(1), 95–102 (1997) H.T. Nguyen, V. Kreinovich, B. Wu, G. Xiang, Computing statistics under interval and fuzzy uncertainty, in Applications to Computer Science and Engineering. Studies in Computational Intelligence 393 (Springer, ...

Publisher: Springer Nature

ISBN: 9783030456191

Category: Technology & Engineering

Page: 265

View: 866

Mainly focusing on processing uncertainty, this book presents state-of-the-art techniques and demonstrates their use in applications to econometrics and other areas. Processing uncertainty is essential, considering that computers – which help us understand real-life processes and make better decisions based on that understanding – get their information from measurements or from expert estimates, neither of which is ever 100% accurate. Measurement uncertainty is usually described using probabilistic techniques, while uncertainty in expert estimates is often described using fuzzy techniques. Therefore, it is important to master both techniques for processing data. This book is highly recommended for researchers and students interested in the latest results and challenges in uncertainty, as well as practitioners who want to learn how to use the corresponding state-of-the-art techniques.

Beyond Traditional Probabilistic Data Processing Techniques Interval Fuzzy etc Methods and Their Applications

H.T. Nguyen, V. Kreinovich, B. Wu, G. Xiang, Computing Statistics Under interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering. (Springer, Berlin, 2012) C.H.Papadimitriou,Computational Complexity, ...

Author: Olga Kosheleva

Publisher: Springer Nature

ISBN: 9783030310417

Category: Computers

Page: 649

View: 194

Data processing has become essential to modern civilization. The original data for this processing comes from measurements or from experts, and both sources are subject to uncertainty. Traditionally, probabilistic methods have been used to process uncertainty. However, in many practical situations, we do not know the corresponding probabilities: in measurements, we often only know the upper bound on the measurement errors; this is known as interval uncertainty. In turn, expert estimates often include imprecise (fuzzy) words from natural language such as "small"; this is known as fuzzy uncertainty. In this book, leading specialists on interval, fuzzy, probabilistic uncertainty and their combination describe state-of-the-art developments in their research areas. Accordingly, the book offers a valuable guide for researchers and practitioners interested in data processing under uncertainty, and an introduction to the latest trends and techniques in this area, suitable for graduate students.

Rough Sets Fuzzy Sets Data Mining and Granular Computing

Towards Faster Estimation of Statistics and ODEs Under Interval, P-Box, and Fuzzy Uncertainty: From Interval Computations to Rough Set-Related Computations Vladik Kreinovich University of Texas at El Paso, El Paso, TX 79968, ...

Author: Sergei O. Kuznetsov

Publisher: Springer

ISBN: 9783642218811

Category: Computers

Page: 370

View: 393

This book constitutes the refereed proceedings of the 13th International Conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing, RSFDGrC 2011, held in Moscow, Russia in June 2011. The 49 revised full papers presented together with 5 invited and 2 tutorial papers were carefully reviewed and selected from a total of 83 submissions. The papers are organized in topical sections on rough sets and approximations, coverings and granules, fuzzy set models, fuzzy set applications, compound values, feature selection and reduction, clusters and concepts, rules and trees, image processing, and interactions and visualization.

2013 International Conference on Computer Science and Artificial Intelligence

We promote a k-means method to fuzzy c-means algorithm and propose a new interval fuzzy clustering algorithm based on ... [12] Nguyen, Hung T., et al, Computing Statistics Under Interval and Fuzzy Uncertainty: Applications to Computer ...

Author: Dr. Yuetong Lin

Publisher: DEStech Publications, Inc

ISBN: 9781605951324

Category: Computers

Page: 460

View: 121

The main objective of ICCSAI2013 is to provide a platform for the presentation of top and latest research results in global scientific areas. The conference aims to provide a high level international forum for researcher, engineers and practitioners to present and discuss recent advances and new techniques in computer science and artificial intelligence. It also serves to foster communications among researcher, engineers and practitioners working in a common interest in improving computer science, artificial intelligence and the related fields. We have received 325 numbers of papers through "Call for Paper", out of which 94 numbers of papers were accepted for publication in the conference proceedings through double blind review process. The conference is designed to stimulate the young minds including Research Scholars, Academicians, and Practitioners to contribute their ideas, thoughts and nobility in these two disciplines.