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Produktbild: Explainable Artificial Intelligence
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Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part II

Aus der Reihe Communications in Computer and Information Science
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79,99 € UVP 90,94 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2024

Abbildungen

XVII, 514 p. 159 illus., 140 illus. in color.

Herausgeber

Luca Longo + weitere

Verlag

Springer

Seitenzahl

514

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

797 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63796-4

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

10.07.2024

Abbildungen

XVII, 514 p. 159 illus., 140 illus. in color.

Herausgeber

Verlag

Springer

Seitenzahl

514

Maße (L/B/H)

23,5/15,5/2,9 cm

Gewicht

797 g

Auflage

2024

Sprache

Englisch

ISBN

978-3-031-63796-4

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: ProductSafety@springernature.com

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  • Produktbild: Explainable Artificial Intelligence

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    .- Graph-Based Interface for Explanations by Examples in Recommender Systems: A User Study.



    .- Explainable AI for Mixed Data Clustering.



    .- Explaining graph classifiers by unsupervised node relevance attribution.



    .- Explaining Clustering of Ecological Momentary Assessment through Temporal and Feature-based Attention.



    .- Graph Edits for Counterfactual Explanations: A comparative study.



    .- Model guidance via explanations turns image classifiers into segmentation models.



    .- Understanding the Dependence of Perception Model Competency on Regions in an Image.



    .- A Guided Tour of Post-hoc XAI Techniques in Image Segmentation.



    .- Explainable Emotion Decoding for Human and Computer Vision.



    .- Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification.




    .- Logic, reasoning, and rule-based explainable AI.



    .- Template Decision Diagrams for Meta Control and Explainability.



    .- A Logic of Weighted Reasons for Explainable Inference in AI.



    .- On Explaining and Reasoning about Fiber Optical Link Problems.



    .- Construction of artificial most representative trees by minimizing tree-based distance measures.



    .- Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles.




    .- Model-agnostic and statistical methods for eXplainable AI.



    .- Observation-specific explanations through scattered data approximation.



    .- CNN-based explanation ensembling for dataset, representation and explanations evaluation.



    .- Local List-wise Explanations of LambdaMART.



    .- Sparseness-Optimized Feature Importance.



    .- Stabilizing Estimates of Shapley Values with Control Variates.



    .- A Guide to Feature Importance Methods for Scientific Inference.



    .- Interpretable Machine Learning for TabPFN.



    .- Statistics and explainability: a fruitful alliance.



    .- How Much Can Stratification Improve the Approximation of Shapley Values?.