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Author: Xizhao Wang Publisher: CRC Press ISBN: 1315353563 Category : Business & Economics Languages : en Pages : 190
Book Description
Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc. Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.
Author: Xizhao Wang Publisher: CRC Press ISBN: 1315353563 Category : Business & Economics Languages : en Pages : 190
Book Description
Learning with uncertainty covers a broad range of scenarios in machine learning, this book mainly focuses on: (1) Decision tree learning with uncertainty, (2) Clustering under uncertainty environment, (3) Active learning based on uncertainty criterion, and (4) Ensemble learning in a framework of uncertainty. The book starts with the introduction to uncertainty including randomness, roughness, fuzziness and non-specificity and then comprehensively discusses a number of key issues in learning with uncertainty, such as uncertainty representation in learning, the influence of uncertainty on the performance of learning system, the heuristic design with uncertainty, etc. Most contents of the book are our research results in recent decades. The purpose of this book is to help the readers to understand the impact of uncertainty on learning processes. It comes with many examples to facilitate understanding. The book can be used as reference book or textbook for researcher fellows, senior undergraduates and postgraduates majored in computer science and technology, applied mathematics, automation, electrical engineering, etc.
Author: G. Williamson McDiarmid Publisher: Routledge ISBN: 1317404505 Category : Education Languages : en Pages : 91
Book Description
Learning for Uncertainty explores technology’s role in education, specifically unpacking the question: How should educators prepare today’s children for a world that has yet to be made? As technology evolves faster than our capacity to fully understand the social, cultural, economic, and moral implications of many innovations, today’s educators are tasked with the unique role of preparing students to capitalize on technology’s opportunities and also mitigate its dangers to their society, to democratic processes, and to institutions. Veteran educators McDiarmid and Zhao explore the implications of emerging technologies for future jobs, organizations, students, and learning, covering topics such as • The future of work and workers as technology eliminates some industries while creating new ones. • Potential futures, both bright and dark, awaiting students. • The qualities, dispositions, social behaviors, and skills that are likely to advantage students in the future. • The possibility of technology to revolutionize education in ways that will better position students for an uncertain future. • How technology can free teachers from time and effort devoted to routine matters to instead assuming roles that are potentially more satisfying and supportive of their students’ learning. • Learning opportunities and educator roles that have the potential to bring about needed changes. • Capitalizing on these uncertain times to rethink curriculum, pedagogy, opportunities to learn, and the organization of school as well as the roles of students, educators, parents, and policymakers. This latest book in the Routledge Leading Change series is ideal reading for educators and policymakers in both P–12 and higher education interested in ensuring our education systems provide the experiences and learning opportunities necessary to cultivate the innovative, iconoclastic, and generative thinkers and creators needed for a future that nourishes the minds, bodies, and spirits of all.
Author: Khalid Saeed Publisher: Springer ISBN: 3642409253 Category : Computers Languages : en Pages : 524
Book Description
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.
Author: James A. Bailey Publisher: Routledge ISBN: 1000385779 Category : Education Languages : en Pages : 135
Book Description
In an increasingly complex context of global pandemic, economic uncertainty, increased racial inequities, and a climate crisis, this practical guide for school leaders explores how capacity as learning at the individual, team, and organizational level can help schools become more agile and adaptive. Author James A. Bailey unpacks a new model of capacity building that combines learning process, leadership mindsets, and skills to enhance learning. This research-based book includes a "Diagnostic for School Learning Capacity" and "Team Considerations" to help school leaders and their teams launch further discussions and advance learning in their contexts. The tools in this timely book are designed to help school leaders handle the increasing volatile, uncertain, complex, and ambiguous world in which schools now exist.
Author: Carole H. Sudre Publisher: Springer Nature ISBN: 3030603652 Category : Computers Languages : en Pages : 233
Book Description
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.
Author: Gerd Gigerenzer Publisher: Penguin UK ISBN: 0140297863 Category : Education Languages : en Pages : 239
Book Description
Are ordinary people able to reason with risk? Detailing case histories and examples, this text presents readers with tools for understanding statistics. In so doing, it encourages us to overcome our innumeracy and empowers us to take responsibility for our own choices.
Author: Hayit Greenspan Publisher: Springer Nature ISBN: 3030326896 Category : Computers Languages : en Pages : 192
Book Description
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.
Author: Kostas Kampourakis Publisher: Oxford University Press ISBN: 0190871687 Category : Science Languages : en Pages : 273
Book Description
Scientific knowledge is the most solid and robust kind of knowledge that humans have because of the self-correcting character inherent in its own processes. Nevertheless, anti-evolutionists, climate denialists, and anti-vaxxers, among others, question some of the best-established scientific findings, making claims that are unsupported by empirical evidence. A common aspect of these claims is the reference to the uncertainties in these areas of research, which leads to the conclusion that science is uncertain about evolution, climate change, and vaccination, among others. The truth of the matter is that while the broad picture is clear, there exist--and will always exist--uncertainties about the details of the respective phenomena. In this book Kampourakis and McCain show that uncertainty is an inherent feature of science that does not devalue it. In contrast, uncertainty actually makes science advance because it motivates further research. The first book of its kind, Uncertainty draws on philosophy of science to explain what uncertainty in science is and how it makes science advance. It contrasts evolution, climate change, and vaccination, where the uncertainties are exaggerated, to genetic testing and forensic science where the uncertainties are usually overlooked. Kampourakis and McCain discuss the scientific, psychological, and philosophical aspects of uncertainty in order to explain what it is really about, what kind of problems it actually poses, and why it ultimately makes science advance. Contrary to the public representations of scientific findings and conclusions that produce an intuitive but distorted view of science as certain, we need to understand and learn to live with uncertainty in science.
Author: Angela Liberatore Publisher: Routledge ISBN: 1134391463 Category : Business & Economics Languages : en Pages : 322
Book Description
This investigative analysis studies why key European countries responded differently to the Chernobyl nuclear disaster, and what can be learned from it. The author details why the accident was defined differently in various countries, why actions were or were not taken, and what was learned about the management of nuclear risk. Furthermore, Liberatore studies the short-term and long-term responses and consequences of Chernobyl not only in specific countries, but within the European Union as a whole. Liberatore also provides a policy communication model to illustrate the interaction among the key personnel in such incidents: the scientists, the politicians, the interest groups, and the mass media. The author's focus upon uncertainty managementis a compelling account for all who seek to understand and improve the practical management of transboundary risks.
Author: Mykel J. Kochenderfer Publisher: MIT Press ISBN: 0262331713 Category : Computers Languages : en Pages : 350
Book Description
An introduction to decision making under uncertainty from a computational perspective, covering both theory and applications ranging from speech recognition to airborne collision avoidance. Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical engineering, and management science. It will also be a valuable professional reference for researchers in a variety of disciplines.
Author: Xizhao Wang
Author: Xizhao Wang
Author: G. Williamson McDiarmid
Author: Khalid Saeed
Author: James A. Bailey
Author: Carole H. Sudre
Author: Gerd Gigerenzer
Author: Hayit Greenspan
Author: Kostas Kampourakis
Author: Angela Liberatore
Author: Mykel J. Kochenderfer