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Scalable Uncertainty Management : 14th International Conference, SUM 2020, Bozen-Bolzano, Italy, September 23-25, 2020, Proceedings / edited by Jesse Davis, Karim Tabia.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence 2945-9141 ; 12322
- Lecture Notes in Artificial Intelligence, 2945-9141 ; 12322
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Computer science-Mathematics.
- Machine learning.
- Computer networks.
- Computer systems.
- Data mining.
- Artificial Intelligence.
- Mathematics of Computing.
- Machine Learning.
- Computer Communication Networks.
- Computer System Implementation.
- Data Mining and Knowledge Discovery.
- Local Subjects:
- Artificial Intelligence.
- Mathematics of Computing.
- Machine Learning.
- Computer Communication Networks.
- Computer System Implementation.
- Data Mining and Knowledge Discovery.
- Physical Description:
- 1 online resource (XIV, 297 pages) : 208 illustrations, 25 illustrations in color.
- Edition:
- 1st ed. 2020.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2020.
- System Details:
- text file PDF
- Summary:
- This book constitutes the refereed proceedings of the 14th International Conference on Scalable Uncertainty Management, SUM 2020, which was held in Bozen-Bolzano, Italy, in September 2020. The 12 full, 7 short papers presented in this volume were carefully reviewed and selected from 30 submissions. Besides that, the book also contains 2 abstracts of invited talks, 2 tutorial papers, and 2 PhD track papers. The conference aims to gather researchers with a common interest in managing and analyzing imperfect information from a wide range of fields, such as artificial intelligence and machine learning, databases, information retrieval and data mining, the semantic web and risk analysis. Due to the Corona pandemic SUM 2020 was held as an virtual event.
- Contents:
- Symbolic Logic Meets Machine Learning: A Brief Survey in Infinite Domains
- Score-Based Explanations in Data Management and Machine Learning
- From Ppossibilistic Rule-Based Systems to Machine Learning
- Logic, Probability and Action: A Situation Calculus Perspective
- When Nominal Analogical Proportions do not Fail
- Measuring Disagreement with Interpolants
- Inferring from an imprecise Plackett-Luce model: Application to Label Ranking
- Inference with Choice Functions Made Practical
- A Formal Learning Theory for Three-way Clustering
- Belief Functions for Safety Arguments Confidence Estimation
- Incremental Elicitation of Capacities for the Sugeno Integral with a Maximum Approach
- Computable Randomness is About More than Probabilities
- Equity in Learning Problems: an OWA Approach
- Conversational Recommender System by Bayesian Methods
- Dealing with Atypical Instances in Evidential Decision-Making
- Evidence Theory Based Combination of Frequent Chronicles for Failure Prediction
- Rule-Based Classification for Evidential Data
- Undecided Voters as Set-Valued Information
- Towards Forecasts under Epistemic Imprecision
- Multi-Dimensional Stable Matching Problems in Abstract Argumentation
- Modal Interpretation of Formal Concept Analysis for Incomplete Representations
- A Symbolic Approach for Counterfactual Explanations
- Modelling Multivariate Ranking Functions with Min-Sum Networks
- An Algorithm for the Contension Inconsistency Measure using Reductions to Answer Set Programming.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-58449-8
- 9783030584498
- Access Restriction:
- Restricted for use by site license.
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