Interpretable Artificial Intelligence: A Perspective of Granular Computing
Published on 29. March 2022
Book
Paperback/Softback
VIII, 429 pages
978-3-030-64951-7 (ISBN)
Description
This book offers a comprehensive treatise on the recent pursuits of Artificial Intelligence (AI) - Explainable Artificial Intelligence (XAI) by casting the crucial features of interpretability and explainability in the original framework of Granular Computing. The innovative perspective established with the aid of information granules provides a high level of human centricity and transparency central to the development of AI constructs. The chapters reflect the breadth of the area and cover recent developments in the methodology, advanced algorithms and applications of XAI to visual analytics, knowledge representation, learning and interpretation. The book appeals to a broad audience including researchers and practitioners interested in gaining exposure to the rapidly growing body of knowledge in AI and intelligent systems.
More details
Series
Edition
2021 ed.
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
32 s/w Abbildungen, 138 farbige Abbildungen
VIII, 429 p. 170 illus., 138 illus. in color.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 24 mm
Weight
663 gr
ISBN-13
978-3-030-64951-7 (9783030649517)
DOI
10.1007/978-3-030-64949-4
Schweitzer Classification
Other editions
Additional editions
Witold Pedrycz | Shyi-Ming Chen
Interpretable Artificial Intelligence: A Perspective of Granular Computing
Book
03/2021
Springer
€171.19
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Persons
Content
Visualizing the Behavior of Convolutional Neural Networks for Time Series Forecasting.- Beyond Deep Event Prediction: Deep Event Understanding based on Explainable Artificial Intelligence.- Interpretation of SVM to build an Explainable AI via Granular Computing.- Factual and Counterfactual Explanation of Fuzzy Information Granules.- Survey of Explainable Machine Learning with Visual and Granular Methods beyond Quasi-explanations.- MiBeX: Malware-inserted Benign Datasets for Explainable Machine Learning.- A Generative Model Based Approach for Zero-shot Breast Cancer Segmentation Explaining Pixels' Contribution to the Model's Prediction.