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E-raamat: Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umea, Sweden, June 19-22, 2023, Proceedings

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Modeling Decisions for Artificial Intelligence: 20th International Conference, MDAI 2023, Umea, Sweden, June 19-22, 2023, ProceedingsSuurem pilt
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This book constitutes the refereed proceedings of the 20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023, held in Umeå, Sweden, during June1922,2023. The 17 papers presented in this volume were carefully reviewed and selected from 28 submissions. Additionally, 1 invited paper were included. The papers discuss different facets of decision processes in a broad sense and present research in data science, data privacy, aggregation functions, human decision making, graphs and social networks, and recommendation and search. The papers are organized in the following topical sections: Decision making and uncertainty; Machine Learning and data science; and Data privacy.
Logic Aggregators and Their Implementations.- Decision making and
uncertainty.- Multi-Target Decision Making under Conditions of Severe
Uncertainty.- Constructive set function and extraction of a k-dimensional
element.- Coherent upper conditional previsions defined by fractal outer
measures to represent the unconscious activity of human brain.- Discrete
chain-based Choquet-like operators.- On a new generalization of decomposition
integrals.- Bipolar OWA operators with continuous input function.- Machine
Learning and data science.- Cost-constrained group feature selection using
information theory.- Conformal Prediction for Accuracy Guarantees in
Classification with Reject Option.- Adapting the Gini's index for solving
Predictive Tasks.- Bayesian logistic model for positive and unlabeled
data.- A goal-oriented specification language for reinforcement
learning.- Improved Spectral Norm Regularization for
NeuralNetworks.- Preprocessing Matters: Automated Pipeline Selection for Fair
Classification.- Predicting Next Whereabouts using Deep Learning.- A
Generalization of Fuzzy c-Means with Variables Controlling Cluster
Size.- Data privacy.- Local Differential Privacy Protocol for Making
Key{Value Data Robust against Poisoning Attacks.- Differential Privacy
through Noise-Graph Addition.
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