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Machine Learning and Knowledge Extraction : 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, Dublin, Ireland, August 25-28, 2020, Proceedings / edited by Andreas Holzinger, Peter Kieseberg, A Min Tjoa, Edgar Weippl.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Information systems and applications, incl. Internet/Web, and HCI ; SL 3, 12279
- Information Systems and Applications, incl. Internet/Web, and HCI ; 12279
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Image processing-Digital techniques.
- Computer vision.
- Software engineering.
- Computers.
- Application software.
- Artificial Intelligence.
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Software Engineering.
- Computing Milieux.
- Computer and Information Systems Applications.
- Local Subjects:
- Artificial Intelligence.
- Computer Imaging, Vision, Pattern Recognition and Graphics.
- Software Engineering.
- Computing Milieux.
- Computer and Information Systems Applications.
- Physical Description:
- 1 online resource (XI, 552 pages) : 171 illustrations, 112 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 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2020, held in Dublin, Ireland, in August 2020. The 30 revised full papers presented were carefully reviewed and selected from 140 submissions. The cross-domain integration and appraisal of different fields provides an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to put these ideas into business for the benefit of humanity. Due to the Corona pandemic CD-MAKE 2020 was held as a virtual event.
- Contents:
- Explainable Artificial Intelligence: concepts, applications, research challenges and visions
- The Explanation Game: Explaining Machine Learning Models Using Shapley Values
- Back to the Feature: a Neural-Symbolic Perspective on Explainable AI
- Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification
- Explainable Reinforcement Learning: A Survey
- A Projected Stochastic Gradient algorithm for estimating Shapley Value applied in attribute importance
- Explaining predictive models with mixed features using Shapley values and conditional inference trees
- Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case
- eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters
- Data Understanding and Interpretation by the Cooperation of Data Analyst and Medical Expert
- A study on the fusion of pixels and patient metadata in CNN-based classification of skin lesion images
- The European legal framework for medical AI
- An Efficient Method for Mining Informative Association Rules in Knowledge Extraction
- Interpretation of SVM using Data Mining Technique to Extract Syllogistic Rules
- Non-Local Second-Order Attention Network For Single Image Super Resolution
- ML-ModelExplorer: An explorative model-agnostic approach to evaluate and compare multi-class classifiers
- Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints
- Scenario-based Requirements Elicitation for User-Centric Explainable AI A Case in Fraud Detection
- On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks
- Active Learning for Auditory Hierarchy
- Improving short text classification through global augmentation methods
- Interpretable Topic Extraction and Word Embedding Learning using row-stochastic DEDICOM
- A Clustering Backed Deep Learning Approach for Document Layout Analysis
- Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias
- Applying AI in Practice: Key Challenges and Lessons Learned
- Function Space Pooling For Graph Convolutional Networks
- Analysis of optical brain signals using connectivity graph networks
- Property-Based Testing for Parameter Learning of Probabilistic Graphical Models
- An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge
- Inter-Space Machine Learning in Smart Environments.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-57321-8
- 9783030573218
- Access Restriction:
- Restricted for use by site license.
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