Modeling Decisions for Artificial Intelligence -

Modeling Decisions for Artificial Intelligence

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

Vicenç Torra, Yasuo Narukawa (Herausgeber)

Buch | Softcover
XVIII, 203 Seiten
2022 | 1st ed. 2022
Springer International Publishing (Verlag)
978-3-031-13447-0 (ISBN)
64,19 inkl. MwSt
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.

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.

Erscheinungsdatum
Reihe/Serie Lecture Notes in Artificial Intelligence
Lecture Notes in Computer Science
Zusatzinfo XVIII, 203 p. 58 illus., 42 illus. in color.
Verlagsort Cham
Sprache englisch
Maße 155 x 235 mm
Gewicht 349 g
Themenwelt Informatik Datenbanken Data Warehouse / Data Mining
Mathematik / Informatik Informatik Netzwerke
Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Informatik Weitere Themen Hardware
Sozialwissenschaften
Schlagworte aggregation functions • Applications • Artificial Intelligence • Computer Networks • Computer Science • Computer Security • Computer systems • conference proceedings • Correlation Analysis • Data Mining • data privacy • Data Security • Fuzzy Logic • Fuzzy Sets • Informatics • machine learning • Network Protocols • Probability • Research • Signal Processing
ISBN-10 3-031-13447-8 / 3031134478
ISBN-13 978-3-031-13447-0 / 9783031134470
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
Haben Sie eine Frage zum Produkt?
Mehr entdecken
aus dem Bereich
Auswertung von Daten mit pandas, NumPy und IPython

von Wes McKinney

Buch | Softcover (2023)
O'Reilly (Verlag)
44,90
Datenanalyse für Künstliche Intelligenz

von Jürgen Cleve; Uwe Lämmel

Buch | Softcover (2024)
De Gruyter Oldenbourg (Verlag)
74,95