UID:
almahu_9949372071602882
Format:
XXII, 225 p. 56 illus., 49 illus. in color.
,
online resource.
Edition:
1st ed. 2022.
ISBN:
9783031157912
Series Statement:
Lecture Notes in Artificial Intelligence ; 13404
Content:
Chapter "Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Note:
An Implementation of Nonmonotonic Reasoning with System W -- Leveraging implicit gaze-based user feedback for interactive machine learning -- The Randomness of Input Data Spaces is an A Priori Predictor for Generalization -- Communicating Safety of Planned Paths via Optimally-Simple Explanations -- Assessing the Accuracy-Explainability-Cost Trade-off on Model Selection for Retail Article Categorization -- Enabling Supervised Machine Learning for SMEs through Data Pooling: A Case Study in the Service Industry -- Unsupervised Alignment of Distributional Word Embeddings. NeuralPDE: Modelling Dynamical Systems from Data -- Deep Neural Networks for Geometric Shape Deformation -- Dynamically Self-Adjusting Gaussian Processes for Data Stream Modelling -- Optimal Fixed-Premise Repairs of EL TBoxes -- Health And Habit: an Agent-based Approach -- Knowledge Graph Embeddings with Ontologies: Reification for Representing Arbitrary Relations -- Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning -- HanKA: Enriched Knowledge Used by an Adaptive Cooking Assistant -- Automated Kantian Ethics: A Faithful Implementation and Testing Framework -- PEBAM: A Profile-based Evaluation Method for Bias Assessment on Mixed Datasets.
In:
Springer Nature eBook
Additional Edition:
Printed edition: ISBN 9783031157905
Additional Edition:
Printed edition: ISBN 9783031157929
Language:
English
DOI:
10.1007/978-3-031-15791-2
URL:
https://doi.org/10.1007/978-3-031-15791-2
