Modeling Decisions for Artificial Intelligence

19th International Conference, MDAI 2022, Sant Cugat, Spain, August 30 – September 2, 2022, Proceedings

AvVicenç Torra,Yasuo Narukawa

Häftad, Engelska, 2022

Del 13408 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2022-07-27
Mått:155 x 235 x 13 mm
Vikt:347 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:203
Förlag:Springer International Publishing AG
ISBN:9783031134470

Utforska kategorier

Artificiell intelligens inom Data och IT
Hårdvara inom Data och IT
Tillämpad datateknik inom Data och IT

Innehållsförteckning

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.