Isabelle Guyon; Alexander Statnikov; Berna Bakir B Cause Effect Pairs in Machine Learning 9783030218096

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Автор: Isabelle Guyon; Alexander Statnikov; Berna Bakir B
Название: Cause Effect Pairs in Machine Learning
ISBN: 9783030218096
Издательство: Springer
Классификация:

Культурология

Искусственный интеллект

Машинное обучение

Распознавание образов

Обработка изображений

ISBN-10: 3030218090
Обложка/Формат: Hardcover
Страницы: 372
Вес: 0.75 кг.
Дата издания: 2019
Серия: The Springer Series on Challenges in Machine Learning
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 90 illustrations, color; 32 illustrations, black and white; xvi, 372 p. 122 illus., 90 illus. in color.
Размер: 234 x 156 x 22
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: This book presents ground-breaking advances in the domain of causal structure learning. The problem of distinguishing cause from effect (“Does altitude cause a change in atmospheric pressure, or vice versa?”) is here cast as a binary classification problem, to be tackled by machine learning algorithms. Based on the results of the ChaLearn Cause-Effect Pairs Challenge, this book reveals that the joint distribution of two variables can be scrutinized by machine learning algorithms to reveal the possible existence of a “causal mechanism”, in the sense that the values of one variable may have been generated from the values of the other.

This book provides both tutorial material on the state-of-the-art on cause-effect pairs and exposes the reader to more advanced material, with a collection of selected papers. Supplemental material includes videos, slides, and code which can be found on the workshop website.

Discovering causal relationships from observational data will become increasingly important in data science with the increasing amount of available data, as a means of detecting potential triggers in epidemiology, social sciences, economy, biology, medicine, and other sciences.

Дополнительное описание: 1. The cause-effect problem: motivation, ideas, and popular misconceptions.- 2. Evaluation methods of cause-effect pairs.- 3. Learning Bivariate Functional Causal Models.- 4. Discriminant Learning Machines.- 5. Cause-Effect Pairs in Time Series with a Foc

Cause Effect Pairs in Machine Learning, Isabelle Guyon; Alexander Statnikov; Berna Bakir B