Modeling Decisions for Artificial Intelligence (eBook, PDF)

19th International Conference, MDAI 2022, Sant Cugat, Spain, August 30 - September 2, 2022, Proceedings
Redaktion: Torra, Vicenç; Narukawa, Yasuo

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 19th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2022, held in Sant Cugat, Spain, during August - September 2022.

The 16 papers presented in this volume were carefully reviewed and selected from 41 submissions.

The papers discuss different facets of decision processes in a broad sense and present research in data science, machine learning, data privacy, aggregation functions, human decision-making, graphs and social networks, and recommendation and search. They were organized in topical sections as follows: Decision making and uncertainty; Data privacy; Machine Learning and data science.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 203
• Erscheinungstermin: 23. August 2022
• Englisch
• ISBN-13: 9783031134487
• Artikelnr.: 65434436

Inhaltsangabe

Decision making and uncertainty.- Optimality Analysis for Stochastic LP Problems.- A Multi-Perceptual-Based Approach for Group Decision Aiding.- Probabilistic Judgement Aggregation by Opinion Update.- Semiring-valued fuzzy rough sets and colour segmentation.- Data privacy.- Bistochastic privacy.- Improvement of Estimate Distribution with Local Differential Privacy.- Geolocated Data Generation and Protection Using Generative Adversarial Net-works.- Machine Learning and data science.- A Strategic Approach based on AND-OR Recommendation Trees for Updating Obsolete Information.- Identification of Subjects Wearing a Surgical Mask from their Speech by means of x-vectors and Fisher Vectors.- Measuring Fairness in Machine Learning models via Counterfactual Examples.- Re-Calibrating Machine Learning Models using Confidence Interval Bounds.- An Analysis of Byzantine-Tolerant Aggregation Mechanisms on Model Poisoning in Federated Learning.- Effective Early Stopping of Point Cloud Neural Networks.- Representation and Interpretability of IE Integral Neural Networks.- Deep Attributed Graph Embeddings.- Estimation of Prediction Error with Regression Trees.

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