Information Uncertainty And Fusion PDF Download
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Author | : Bernadette Bouchon-Meunier |
Publisher | : Springer Science & Business Media |
Total Pages | : 456 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461552095 |
Download Information, Uncertainty and Fusion Book in PDF, ePub and Kindle
As we stand at the precipice of the twenty first century the ability to capture and transmit copious amounts of information is clearly a defining feature of the human race. In order to increase the value of this vast supply of information we must develop means for effectively processing it. Newly emerging disciplines such as Information Engineering and Soft Computing are being developed in order to provide the tools required. Conferences such as the International Conference on Information Processing and ManagementofUncertainty in Knowledge-based Systems (IPMU) are being held to provide forums in which researchers can discuss the latest developments. The recent IPMU conference held at La Sorbonne in Paris brought together some of the world's leading experts in uncertainty and information fusion. In this volume we have included a selection ofpapers from this conference. What should be clear from looking at this volume is the number of different ways that are available for representing uncertain information. This variety in representational frameworks is a manifestation of the different types of uncertainty that appear in the information available to the users. Perhaps, the representation with the longest history is probability theory. This representation is best at addressing the uncertainty associated with the occurrence of different values for similar variables. This uncertainty is often described as randomness. Rough sets can be seen as a type of uncertainty that can deal effectively with lack of specificity, it is a powerful tool for manipulating granular information.
Author | : Alain Appriou |
Publisher | : John Wiley & Sons |
Total Pages | : 288 |
Release | : 2014-07-09 |
Genre | : Technology & Engineering |
ISBN | : 1118578678 |
Download Uncertainty Theories and Multisensor Data Fusion Book in PDF, ePub and Kindle
Addressing recent challenges and developments in this growing field, Multisensor Data Fusion Uncertainty Theory first discusses basic questions such as: Why and when is multiple sensor fusion necessary? How can the available measurements be characterized in such a case? What is the purpose and the specificity of information fusion processing in multiple sensor systems? Considering the different uncertainty formalisms, a set of coherent operators corresponding to the different steps of a complete fusion process is then developed, in order to meet the requirements identified in the first part of the book.
Author | : Wenhui Liao |
Publisher | : |
Total Pages | : 278 |
Release | : 2006 |
Genre | : Electrical, computer, and systems engineering |
ISBN | : |
Download Dynamic and Active Information Fusion for Decision Making Under Uncertainty Book in PDF, ePub and Kindle
Author | : Xue-Bo Jin |
Publisher | : MDPI |
Total Pages | : 602 |
Release | : 2020-03-23 |
Genre | : Technology & Engineering |
ISBN | : 3039283022 |
Download Multi-Sensor Information Fusion Book in PDF, ePub and Kindle
This book includes papers from the section “Multisensor Information Fusion”, from Sensors between 2018 to 2019. It focuses on the latest research results of current multi-sensor fusion technologies and represents the latest research trends, including traditional information fusion technologies, estimation and filtering, and the latest research, artificial intelligence involving deep learning.
Author | : Paul K. Davis |
Publisher | : Rand Corporation |
Total Pages | : 0 |
Release | : 2016 |
Genre | : History |
ISBN | : |
Download Uncertainty-sensitive Heterogeneous Information Fusion Book in PDF, ePub and Kindle
Presents research on methods for heterogeneous information fusion--combining data that are qualitative, subjective, fuzzy, ambiguous, contradictory, and even deceptive, in order to form a realistic assessment of threat in a counterterrorism context.
Author | : Eyke Hüllermeier |
Publisher | : Springer Science & Business Media |
Total Pages | : 786 |
Release | : 2010-06-25 |
Genre | : Computers |
ISBN | : 3642140548 |
Download Information Processing and Management of Uncertainty in Knowledge-Based Systems Book in PDF, ePub and Kindle
The International Conference on Information Processing and Management of - certainty in Knowledge-Based Systems, IPMU, is organized every two years with the aim of bringing together scientists working on methods for the management of uncertainty and aggregation of information in intelligent systems. Since 1986, this conference has been providing a forum for the exchange of ideas between th theoreticians and practitioners working in these areas and related ?elds. The 13 IPMU conference took place in Dortmund, Germany, June 28–July 2, 2010. This volume contains 79 papers selected through a rigorous reviewing process. The contributions re?ect the richness of research on topics within the scope of the conference and represent several important developments, speci?cally focused on theoretical foundations and methods for information processing and management of uncertainty in knowledge-based systems. We were delighted that Melanie Mitchell (Portland State University, USA), Nihkil R. Pal (Indian Statistical Institute), Bernhard Sch ̈ olkopf (Max Planck I- titute for Biological Cybernetics, Tubing ̈ en, Germany) and Wolfgang Wahlster (German Research Center for Arti?cial Intelligence, Saarbruc ̈ ken) accepted our invitations to present keynote lectures. Jim Bezdek received the Kamp ́ede F ́ eriet Award, granted every two years on the occasion of the IPMU conference, in view of his eminent research contributions to the handling of uncertainty in clustering, data analysis and pattern recognition.
Author | : Youssef Bazzi (Ahmad) |
Publisher | : |
Total Pages | : 240 |
Release | : 1990 |
Genre | : |
ISBN | : |
Download Reasoning Under Uncertainty with Dependency Information in Sensor Fusion Book in PDF, ePub and Kindle
Author | : David Hall |
Publisher | : CRC Press |
Total Pages | : 564 |
Release | : 2001-06-20 |
Genre | : Technology & Engineering |
ISBN | : 1420038540 |
Download Multisensor Data Fusion Book in PDF, ePub and Kindle
The emerging technology of multisensor data fusion has a wide range of applications, both in Department of Defense (DoD) areas and in the civilian arena. The techniques of multisensor data fusion draw from an equally broad range of disciplines, including artificial intelligence, pattern recognition, and statistical estimation. With the rapid evolut
Author | : |
Publisher | : |
Total Pages | : 35 |
Release | : 2003 |
Genre | : |
ISBN | : |
Download Combating Uncertainty With Fusion Book in PDF, ePub and Kindle
This report is a summary of a NASA/ONR-sponsored workshop, Combating Uncertainty with Fusion, that was organized in Woods Hole in April 2002. The main purpose of the workshop was to address a class of difficult computational problems that are characterized by combining large amounts of data or datasets from diverse sources that are related in complex, stochastic, and poorly understood ways. The intent was to determine whether understanding of biological fusion processes could provide guidance to the development of robust algorithms that would alleviate the difficulties encountered in a variety of application areas including the Earth Observation System.
Author | : Mohammad Ghavamzadeh |
Publisher | : |
Total Pages | : 146 |
Release | : 2015-11-18 |
Genre | : Computers |
ISBN | : 9781680830880 |
Download Bayesian Reinforcement Learning Book in PDF, ePub and Kindle
Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.