UID:
almahu_9949450766502882
Format:
XVIII, 203 p. 58 illus., 42 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783031134487
Series Statement:
Lecture Notes in Artificial Intelligence, 13408
Content:
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.
Note:
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.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031134470
Additional Edition:
Printed edition: ISBN 9783031134494
Language:
English
DOI:
10.1007/978-3-031-13448-7
URL:
https://doi.org/10.1007/978-3-031-13448-7
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