Uncertainty Modelling in Data Science

Nonfiction, Computers, Advanced Computing, Artificial Intelligence, General Computing
Cover of the book Uncertainty Modelling in Data Science by , Springer International Publishing
View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart
Author: ISBN: 9783319975474
Publisher: Springer International Publishing Publication: July 24, 2018
Imprint: Springer Language: English
Author:
ISBN: 9783319975474
Publisher: Springer International Publishing
Publication: July 24, 2018
Imprint: Springer
Language: English

This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair.

Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs.

The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them.

Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.

View on Amazon View on AbeBooks View on Kobo View on B.Depository View on eBay View on Walmart

This book features 29 peer-reviewed papers presented at the 9th International Conference on Soft Methods in Probability and Statistics (SMPS 2018), which was held in conjunction with the 5th International Conference on Belief Functions (BELIEF 2018) in Compiègne, France on September 17–21, 2018. It includes foundational, methodological and applied contributions on topics as varied as imprecise data handling, linguistic summaries, model coherence, imprecise Markov chains, and robust optimisation. These proceedings were produced using EasyChair.

Over recent decades, interest in extensions and alternatives to probability and statistics has increased significantly in diverse areas, including decision-making, data mining and machine learning, and optimisation. This interest stems from the need to enrich existing models, in order to include different facets of uncertainty, like ignorance, vagueness, randomness, conflict or imprecision. Frameworks such as rough sets, fuzzy sets, fuzzy random variables, random sets, belief functions, possibility theory, imprecise probabilities, lower previsions, and desirable gambles all share this goal, but have emerged from different needs.

The advances, results and tools presented in this book are important in the ubiquitous and fast-growing fields of data science, machine learning and artificial intelligence. Indeed, an important aspect of some of the learned predictive models is the trust placed in them.

Modelling the uncertainty associated with the data and the models carefully and with principled methods is one of the means of increasing this trust, as the model will then be able to distinguish between reliable and less reliable predictions. In addition, extensions such as fuzzy sets can be explicitly designed to provide interpretable predictive models, facilitating user interaction and increasing trust.

More books from Springer International Publishing

Cover of the book Whither Turbulence and Big Data in the 21st Century? by
Cover of the book E-Business and Telecommunications by
Cover of the book Power-to-Gas: Technology and Business Models by
Cover of the book Design of Power-Efficient Highly Digital Analog-to-Digital Converters for Next-Generation Wireless Communication Systems by
Cover of the book Transformational Sales by
Cover of the book Interleukin 12: Antitumor Activity and Immunotherapeutic Potential in Oncology by
Cover of the book Physically Unclonable Functions by
Cover of the book You Must Be Very Intelligent by
Cover of the book Immersive Theatre and Audience Experience by
Cover of the book Atlas of Swept Source Optical Coherence Tomography by
Cover of the book Climate Change Adaptation Strategies – An Upstream-downstream Perspective by
Cover of the book Computational Electromagnetism by
Cover of the book Quantitative Modeling of Operational Risk in Finance and Banking Using Possibility Theory by
Cover of the book Safe Use of Wastewater in Agriculture by
Cover of the book HCI Redux by
We use our own "cookies" and third party cookies to improve services and to see statistical information. By using this website, you agree to our Privacy Policy