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.
Author: Xizhao Wang
Publisher: CRC Press
Category: Business & Economics
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.
"This is an important book, full of relevant examples and worrying case histories.
Author: Gerd Gigerenzer
Publisher: Penguin UK
"This is an important book, full of relevant examples and worrying case histories. By the end of it, the reader has been presented with a powerful set of tools for understanding statistics...anyone who wants to take responsibly for their own medicalchoices should read it" - New Scientist However much we crave certainty, we live in an uncertain world. But are we guilty of wildly exaggerating the chances of some unwanted event happening to us? Are ordinary people idiots when reasoning with risk? Far too many of us, argues Gerd Gigerenzer, are hampered by our own innumeracy. Here, he shows us that our difficulties in thinking about numbers can easily be overcome.
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 ...
Author: Hayit Greenspan
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.
This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models, focusing mainly on the development and testing of two different models.
Author: Durga Lal Shrestha
Publisher: CRC Press
This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models, focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological model�s performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty.
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: Angela Liberatore
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.
The models applied in this book demonstrate that fundamental changes must occur to transform today's power sector into a more sustainable one over the course of this century.
Author: Fabian Wagner
Publisher: Springer Science & Business Media
The book examines the future deployment of renewable power from a normative point of view. It identifies properties characterizing the cost-optimal transition towards a renewable power system and analyzes the key drivers behind this transition. Among those drivers, particular attention is paid to technological cost reductions and the implications of uncertainty. From a methodological perspective, the main contributions of this book relate to the field of endogenous learning and uncertainty in optimizing energy system models. The primary objective here is closing the gap between the strand of literature covering renewable potential analyses on the one side and energy system modeling with endogenous technological change on the other side. The models applied in this book demonstrate that fundamental changes must occur to transform today's power sector into a more sustainable one over the course of this century. Apart from its methodological contributions, this work is also intended to provide practically relevant insights regarding the long-term competitiveness of renewable power generation.
The book presents an overview of precisely what it is that makes a situation uncertain by integrating key ideas from a variety of research disciplines – engineering, psychology, human factors, computer science, and neuroscience.
Author: Magda Osman
Publisher: John Wiley & Sons
Controlling Uncertainty: Decision Making and Learning in Complex Worlds reviews and discusses the most current research relating to the ways we can control the uncertain world around us. Features reviews and discussions of the most current research in a number of fields relevant to controlling uncertainty, such as psychology, neuroscience, computer science and engineering Presents a new framework that is designed to integrate a variety of disparate fields of research Represents the first book of its kind to provide a general overview of work related to understanding control
We apply this work to medical image segmentation, specifically the task of segmenting glioblastoma (GBM) in brain magnetic resonance imaging (MRI) scans.
Author: Sameer Tharakan
Kernel methods are a broad class of algorithms that are applied in a host of scientific computing fields. In this thesis we focus on applying kernel methods to supervised learning in machine learning and uncertainty quantification of learning algorithms. Kernel methods offer an interpretable way to model nonlinear functions, but they are difficult to scale due to the computational challenges. As a result, kernels are underutilized as tools for solving supervised learning problems on large data and measuring uncertainty in learning algorithms. We first provide understanding and methods to effectively scale kernel methods for supervised learning and to then use those results to inform uncertainty quantification of machine learning algorithms in medical image segmentation. The primary challenge of scaling kernel methods is computing and applying the kernel matrix, so approximating this matrix is a crucial step for all kernel methods. The primary approximation method studied in this thesis is the Nystrom method, a popular, easy-to- implement method which uses randomized sampling to construct a low-rank factorization of the matrix. We implement a parallel version of Nystrom and use it to study properties of large kernel matrices and offer both theoretical and empirical comparisons of Nystrom and treecodes (a more sophisticated matrix approximation technique); these results determine the set of problems to which Nystrom is best suited. Applying an approximation to a kernel learning problem generally results in convex optimization problems, but only gradient-based methods are effectively scaled. We explore the efficacy of methods that use second derivative information by deriving a novel formulation and implementing a solver for a supervised learning classification method called kernel logistic regression (KLR). Our work combines Nystrom with an Inexact Newton solver to effectively scale to large datasets and outperform a state-of-the-art gradient solver. We apply this work to medical image segmentation, specifically the task of segmenting glioblastoma (GBM) in brain magnetic resonance imaging (MRI) scans. Segmenting GBM is an important task in tumor prognosis and research efforts, but segmenting an image is a manual, time-consuming process. As a result, researchers have developed sophisticated methods to automate the process; deep neural networks (DNNs) can now achieve results close to human accuracy. DNNs represent the composition of a highly nonlinear representation of the data through millions of parameters and logistic regression on the results for each pixel. However, DNNs do not offer informative uncertainty estimates, which limits the adoption of these methods. We address the lack of uncertainty by using the KLR method to replace the last layer in the DNN. This approximation offers significantly smoother probability maps and, while reducing the parameter space by orders of magnitude, does not severely weaken the performance of the algorithm. The main benefit of our approach is that it provides an easy-to-sample, approximate posterior distribution of the KLR weights. We investigate the structure of this posterior and demonstrate that the uncertainty estimates produced by sampling the posterior empirically approximate DNN errors well
Probability is the bedrock of machine learning. You cannot develop a deep understanding and application of machine learning without it. Cut through the equations, Greek letters, and confusion, and discover the topics in probability that you need to know. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover the importance of probability to machine learning, Bayesian probability, entropy, density estimation, maximum likelihood, and much more.
"This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels.
Author: Sandra Bohlinger
"This book analyses the challenges of globalisation and uncertainty impacting on working and learning at individual, organisational and societal levels. Each of the contributions addresses two overall questions: How is working and learning affected by uncertainty and globalisation? And, in what ways do individuals, organisations, political actors and education systems respond to these challenges?Part 1 focuses on the micro level of working and learning for understanding the learning processes from an individual point of view by reflecting on learners’ needs and situations at work and in school-work transitions. Part 2 addresses the meso level by discussing sector-specific and organisational approaches to working and learning in times of uncertainty. The chapters represent a broad range of branches including public services (police work), the automotive sector and the health sector (elderly care). Finally, Part 3 addresses the macro level of working and learning by analysing how to govern, structure and organise vocational, professional and adult education at the boundaries of work, education and policy making."
Author: Mark William JohnsonPublish On: 2019-02-03
The book will be of interest to scholars of education and technology, as well as those interested in the practical technological issues faced by all universities both now and in the future.
Author: Mark William Johnson
What has the computer done to education? What does the future hold for universities in its wake? Uncertain Education argues that the impact of technology on education has resulted in ever-increasing uncertainty where universities are in a positive-feedback loop from which they will not escape without radical transformation. Mark Johnson presents an analysis of uncertainty in education using the science of cybernetics. Drawing on the cybernetic approach to examining whole systems, he details a methodology for conceiving of the sustainable relationship between technology and the institution of education in an environment of uncertainty. In showing how cybernetic insights can shed light on education's current and future state, it pinpoints where future developments in global education lie, and where society's technologically-produced uncertainty will take us. The book will be of interest to scholars of education and technology, as well as those interested in the practical technological issues faced by all universities both now and in the future.
Winning Principles is part a story of life and part business. The book chronicles the author's journey in life and also his journey through business.
Author: D.C. Work
Publisher: Christian Faith Publishing, Inc.
Category: Business & Economics
Winning Principles is part a story of life and part business. The book chronicles the author's journey in life and also his journey through business. Through the book the author not only shares parts of his journeys but also shares with the reader lessons learned, which the author has developed into essential business principles. The author gives a detailed explanation of these principles and shows the importance of applying each. Anyone looking to improve their business or themselves individually will benefit from this read. Starting as a young man with a young family, the journey begins with a life-altering accident that forces the author to change professions and face a future of uncertainty. Along the journey, the author realizes how a little faith can lead to big things as God continually opened doors, gave insight, and ultimately blessed the author and his family. It is a story of the importance of having faith and trusting in God to provide, not to look at your current circumstance but to focus on your relationship with the Lord and keep moving. This book also provides lessons in business that the author has learned over twenty years in the areas of leadership, team work, mentoring, business development, and a commitment to the business as a whole. The author explains not only how to apply these principles but also the importance of applying these principles to your company. Whether you are someone who enjoys a testimony of the faithfulness of the Lord or someone who is looking for some insight into business strategies, this book will cover it all.
This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning.
Author: Zengchang Qin
Machine learning and data mining are inseparably connected with uncertainty. The observable data for learning is usually imprecise, incomplete or noisy. Uncertainty Modeling for Data Mining: A Label Semantics Approach introduces 'label semantics', a fuzzy-logic-based theory for modeling uncertainty. Several new data mining algorithms based on label semantics are proposed and tested on real-world datasets. A prototype interpretation of label semantics and new prototype-based data mining algorithms are also discussed. This book offers a valuable resource for postgraduates, researchers and other professionals in the fields of data mining, fuzzy computing and uncertainty reasoning. Zengchang Qin is an associate professor at the School of Automation Science and Electrical Engineering, Beihang University, China; Yongchuan Tang is an associate professor at the College of Computer Science, Zhejiang University, China.
Author: International Monetary FundPublish On: 1988-12-27
The paper considers gains from international economic policy coordination when there is uncertainty concerning the functioning of the world economy, but also learning about the “true” model on the part of policymakers.
Author: International Monetary Fund
Publisher: International Monetary Fund
Category: Business & Economics
The paper considers gains from international economic policy coordination when there is uncertainty concerning the functioning of the world economy, but also learning about the “true” model on the part of policymakers. The paper reports estimates of plausible alternative versions of a standard, two-country model. Activist policy (either coordinated or uncoordinated) may produce large welfare losses in the absence of learning, if policymakers believe in the wrong model; hence exogenous money targets and freely flexible exchange rates may be best. However, model learning (from observations on macroeconomic variables) causes coordinated policies to dominate activist uncoordinated policies or exogenous money targets.