Olga Kosheleva – författare

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, suitablefor graduate students.

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, suitablefor graduate students. 

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, suitablefor graduate students.

This book explains how to teach better and presents the latest research on processing educational data and presents traditional statistical techniques as well as probabilistic, interval, and fuzzy approaches. Teaching is a very rewarding activity; it is also a very difficult one – because it is largely an art. There is a lot of advice on teaching available, but it is usually informal and is not easy to follow. To remedy this situation, it is reasonable to use techniques specifically designed to handle such imprecise knowledge: the fuzzy logic techniques.Since there are a large number of statistical studies of different teaching techniques, the authors combined statistical and fuzzy approaches to process the educational data in order to provide insights into improving all the stages of the education process: from forming a curriculum to deciding in which order to present the material to grading the assignments and exams.The authors do not claim to have solved all the problems of education. Instead they show, using numerous examples, that an innovative combination of different uncertainty techniques can improve teaching. The book offers teachers and instructors valuable advice and provides researchers in pedagogical and fuzzy areas with techniques to further advance teaching.

This book explains how to teach better and presents the latest research on processing educational data and presents traditional statistical techniques as well as probabilistic, interval, and fuzzy approaches. Teaching is a very rewarding activity; it is also a very difficult one – because it is largely an art. There is a lot of advice on teaching available, but it is usually informal and is not easy to follow. To remedy this situation, it is reasonable to use techniques specifically designed to handle such imprecise knowledge: the fuzzy logic techniques.

Since there are a large number of statistical studies of different teaching techniques, the authors combined statistical and fuzzy approaches to process the educational data in order to provide insights into improving all the stages of the education process: from forming a curriculum to deciding in which order to present the material to grading the assignments and exams.

The authors do not claim to have solved all the problems of education. Instead they show, using numerous examples, that an innovative combination of different uncertainty techniques can improve teaching. The book offers teachers and instructors valuable advice and provides researchers in pedagogical and fuzzy areas with techniques to further advance teaching.