Uncertainty in Artificial Intelligence 4

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Publisher : Elsevier
ISBN 13 : 1483296547
Total Pages : 435 pages
Book Rating : 4.48/5 ( download)

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Book Synopsis Uncertainty in Artificial Intelligence 4 by : T.S. Levitt

Download or read book Uncertainty in Artificial Intelligence 4 written by T.S. Levitt and published by Elsevier. This book was released on 2014-06-28 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: Clearly illustrated in this volume is the current relationship between Uncertainty and AI.It has been said that research in AI revolves around five basic questions asked relative to some particular domain: What knowledge is required? How can this knowledge be acquired? How can it be represented in a system? How should this knowledge be manipulated in order to provide intelligent behavior? How can the behavior be explained? In this volume, all of these questions are addressed. From the perspective of the relationship of uncertainty to the basic questions of AI, the book divides naturally into four sections which highlight both the strengths and weaknesses of the current state of the relationship between Uncertainty and AI.

Artificial Intelligence with Uncertainty

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Publisher : CRC Press
ISBN 13 : 1498776272
Total Pages : 290 pages
Book Rating : 4.71/5 ( download)

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Book Synopsis Artificial Intelligence with Uncertainty by : Deyi Li

Download or read book Artificial Intelligence with Uncertainty written by Deyi Li and published by CRC Press. This book was released on 2017-05-18 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops a framework that shows how uncertainty in Artificial Intelligence (AI) expands and generalizes traditional AI. It explores the uncertainties of knowledge and intelligence. The authors focus on the importance of natural language – the carrier of knowledge and intelligence, and introduce efficient physical methods for data mining amd control. In this new edition, we have more in-depth description of the models and methods, of which the mathematical properties are proved strictly which make these theories and methods more complete. The authors also highlight their latest research results.

Uncertainty in Artificial Intelligence 5

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Publisher : North Holland
ISBN 13 : 9780444887382
Total Pages : 459 pages
Book Rating : 4.85/5 ( download)

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Book Synopsis Uncertainty in Artificial Intelligence 5 by : Max Henrion

Download or read book Uncertainty in Artificial Intelligence 5 written by Max Henrion and published by North Holland. This book was released on 1990-01 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume, like its predecessors, reflects the cutting edge of research on the automation of reasoning under uncertainty. A more pragmatic emphasis is evident, for although some papers address fundamental issues, the majority address practical issues. Topics include the relations between alternative formalisms (including possibilistic reasoning), Dempster-Shafer belief functions, non-monotonic reasoning, Bayesian and decision theoretic schemes, and new inference techniques for belief nets. New techniques are applied to important problems in medicine, vision, robotics, and natural language understanding.

Computer Information Systems and Industrial Management

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Publisher : Springer
ISBN 13 : 3642409253
Total Pages : 541 pages
Book Rating : 4.57/5 ( download)

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Book Synopsis Computer Information Systems and Industrial Management by : Khalid Saeed

Download or read book Computer Information Systems and Industrial Management written by Khalid Saeed and published by Springer. This book was released on 2013-09-20 with total page 541 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 12th IFIP TC 8 International Conference, CISIM 2013, held in Cracow, Poland, in September 2013. The 44 papers presented in this volume were carefully reviewed and selected from over 60 submissions. They are organized in topical sections on biometric and biomedical applications; pattern recognition and image processing; various aspects of computer security, networking, algorithms, and industrial applications. The book also contains full papers of a keynote speech and the invited talk.

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures

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Publisher : Springer Nature
ISBN 13 : 3030326896
Total Pages : 192 pages
Book Rating : 4.90/5 ( download)

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Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures by : Hayit Greenspan

Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures written by Hayit Greenspan and published by Springer Nature. This book was released on 2019-10-10 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2019, and the 8th International Workshop on Clinical Image-Based Procedures, CLIP 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. For UNSURE 2019, 8 papers from 15 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. CLIP 2019 accepted 11 papers from the 15 submissions received. The workshops provides a forum for work centred on specific clinical applications, including techniques and procedures based on comprehensive clinical image and other data.

Uncertainty in Artificial Intelligence

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Author :
Publisher : Morgan Kaufmann
ISBN 13 : 1483282872
Total Pages : 379 pages
Book Rating : 4.79/5 ( download)

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Book Synopsis Uncertainty in Artificial Intelligence by : Didier J. Dubois

Download or read book Uncertainty in Artificial Intelligence written by Didier J. Dubois and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 379 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in Artificial Intelligence: Proceedings of the Eighth Conference (1992) covers the papers presented at the Eighth Conference on Uncertainty in Artificial Intelligence, held at Stanford University on July 17-19, 1992. The book focuses on the processes, methodologies, technologies, and approaches involved in artificial intelligence. The selection first offers information on Relative Evidential Support (RES), modal logics for qualitative possibility and beliefs, and optimizing causal orderings for generating DAGs from data. Discussions focus on reversal, swap, and unclique operators, modal representation of possibility, and beliefs and conditionals. The text then examines structural controllability and observability in influence diagrams, lattice-based graded logic, and dynamic network models for forecasting. The manuscript takes a look at reformulating inference problems through selective conditioning, entropy and belief networks, parallelizing probabilistic inference, and a symbolic approach to reasoning with linguistic quantifiers. The text also ponders on sidestepping the triangulation problem in Bayesian net computations; exploring localization in Bayesian networks for large expert systems; and expressing relational and temporal knowledge in visual probabilistic networks. The selection is a valuable reference for researchers interested in artificial intelligence.

Uncertainty in Artificial Intelligence

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Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.16/5 ( download)

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Book Synopsis Uncertainty in Artificial Intelligence by : American Association for Artificial Intelligence

Download or read book Uncertainty in Artificial Intelligence written by American Association for Artificial Intelligence and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis

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Publisher : Springer Nature
ISBN 13 : 3030603652
Total Pages : 233 pages
Book Rating : 4.56/5 ( download)

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Book Synopsis Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis by : Carole H. Sudre

Download or read book Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis written by Carole H. Sudre and published by Springer Nature. This book was released on 2020-10-05 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Second International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2020, and the Third International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2020, held in conjunction with MICCAI 2020, in Lima, Peru, in October 2020. The workshops were held virtually due to the COVID-19 pandemic. For UNSURE 2020, 10 papers from 18 submissions were accepted for publication. They focus on developing awareness and encouraging research in the field of uncertainty modelling to enable safe implementation of machine learning tools in the clinical world. GRAIL 2020 accepted 10 papers from the 12 submissions received. The workshop aims to bring together scientists that use and develop graph-based models for the analysis of biomedical images and to encourage the exploration of graph-based models for difficult clinical problems within a variety of biomedical imaging contexts.

Uncertainty in Artificial Intelligence

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Author :
Publisher : Morgan Kaufmann
ISBN 13 : 1483214516
Total Pages : 554 pages
Book Rating : 4.11/5 ( download)

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Book Synopsis Uncertainty in Artificial Intelligence by : David Heckerman

Download or read book Uncertainty in Artificial Intelligence written by David Heckerman and published by Morgan Kaufmann. This book was released on 2014-05-12 with total page 554 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in Artificial Intelligence contains the proceedings of the Ninth Conference on Uncertainty in Artificial Intelligence held at the Catholic University of America in Washington, DC, on July 9-11, 1993. The papers focus on methods of reasoning and decision making under uncertainty as applied to problems in artificial intelligence (AI) and cover topics ranging from knowledge acquisition and automated model construction to learning, planning, temporal reasoning, and machine vision. Comprised of 66 chapters, this book begins with a discussion on causality in Bayesian belief networks before turning to a decision theoretic account of conditional ought statements that rectifies glaring deficiencies in classical deontic logic and forms a sound basis for qualitative decision theory. Subsequent chapters explore trade-offs in constructing and evaluating temporal influence diagrams; normative engineering risk management systems; additive belief-network models; and sensitivity analysis for probability assessments in Bayesian networks. Automated model construction and learning as well as algorithms for inference and decision making are also considered. This monograph will be of interest to both students and practitioners in the fields of AI and computer science.

Epistemic Uncertainty in Artificial Intelligence

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Publisher : Springer
ISBN 13 : 9783031579622
Total Pages : 0 pages
Book Rating : 4.23/5 ( download)

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Book Synopsis Epistemic Uncertainty in Artificial Intelligence by : Fabio Cuzzolin

Download or read book Epistemic Uncertainty in Artificial Intelligence written by Fabio Cuzzolin and published by Springer. This book was released on 2024-06-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This LNCS 14523 conference volume constitutes the proceedings of the First International Workshop, Epi UAI 2023, in Pittsburgh, PA, USA, August 2023. The 8 full papers together included in this volume were carefully reviewed and selected from 16 submissions. Epistemic AI focuses, in particular, on some of the most important areas of machine learning: unsupervised learning, supervised learning, and reinforcement learning.