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Interpretable Artificial Intelligence: A Perspective of Granular Computing 2021 ed. [Pehme köide]

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  • Formaat: Paperback / softback, 429 pages, kõrgus x laius: 235x155 mm, kaal: 670 g, 138 Illustrations, color; 32 Illustrations, black and white; VIII, 429 p. 170 illus., 138 illus. in color., 1 Paperback / softback
  • Sari: Studies in Computational Intelligence 937
  • Ilmumisaeg: 29-Mar-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030649512
  • ISBN-13: 9783030649517
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  • Formaat: Paperback / softback, 429 pages, kõrgus x laius: 235x155 mm, kaal: 670 g, 138 Illustrations, color; 32 Illustrations, black and white; VIII, 429 p. 170 illus., 138 illus. in color., 1 Paperback / softback
  • Sari: Studies in Computational Intelligence 937
  • Ilmumisaeg: 29-Mar-2022
  • Kirjastus: Springer Nature Switzerland AG
  • ISBN-10: 3030649512
  • ISBN-13: 9783030649517
Teised raamatud teemal:
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.
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 Models Prediction.