Modeling Decisions for Artificial Intelligence 20th International Conference, MDAI 2023, Umeå, Sweden, June 19–22, 2023, Proceedings
This book constitutes the refereed proceedings of the 20th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2023, held in Umeå, Sweden, during June19–22,2023. The 17 papers presented in this volume were carefully reviewed and selected from 28 submissions. Additionally...
| Other Authors: | , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer Nature Switzerland
2023, 2023
|
| Edition: | 1st ed. 2023 |
| Series: | Lecture Notes in Artificial Intelligence
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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