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Machine Learning and Knowledge Extraction

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Cover of 'Machine Learning and Knowledge Extraction'

Table of Contents

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    Book Overview
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    Chapter 1 Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions
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    Chapter 2 The Explanation Game: Explaining Machine Learning Models Using Shapley Values
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    Chapter 3 Back to the Feature: A Neural-Symbolic Perspective on Explainable AI
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    Chapter 4 Explain Graph Neural Networks to Understand Weighted Graph Features in Node Classification
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    Chapter 5 Explainable Reinforcement Learning: A Survey
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    Chapter 6 A Projected Stochastic Gradient Algorithm for Estimating Shapley Value Applied in Attribute Importance
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    Chapter 7 Explaining Predictive Models with Mixed Features Using Shapley Values and Conditional Inference Trees
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    Chapter 8 Explainable Deep Learning for Fault Prognostics in Complex Systems: A Particle Accelerator Use-Case
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    Chapter 9 eXDiL: A Tool for Classifying and eXplaining Hospital Discharge Letters
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    Chapter 10 Cooperation Between Data Analysts and Medical Experts: A Case Study
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    Chapter 11 A Study on the Fusion of Pixels and Patient Metadata in CNN-Based Classification of Skin Lesion Images
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    Chapter 12 The European Legal Framework for Medical AI
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    Chapter 13 An Efficient Method for Mining Informative Association Rules in Knowledge Extraction
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    Chapter 14 Interpretation of SVM Using Data Mining Technique to Extract Syllogistic Rules
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    Chapter 15 Non-local Second-Order Attention Network for Single Image Super Resolution
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    Chapter 16 ML-ModelExplorer: An Explorative Model-Agnostic Approach to Evaluate and Compare Multi-class Classifiers
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    Chapter 17 Subverting Network Intrusion Detection: Crafting Adversarial Examples Accounting for Domain-Specific Constraints
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    Chapter 18 Scenario-Based Requirements Elicitation for User-Centric Explainable AI
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    Chapter 19 On-the-fly Black-Box Probably Approximately Correct Checking of Recurrent Neural Networks
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    Chapter 20 Active Learning for Auditory Hierarchy
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    Chapter 21 Improving Short Text Classification Through Global Augmentation Methods
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    Chapter 22 Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM
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    Chapter 23 A Clustering Backed Deep Learning Approach for Document Layout Analysis
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    Chapter 24 Calibrating Human-AI Collaboration: Impact of Risk, Ambiguity and Transparency on Algorithmic Bias
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    Chapter 25 Applying AI in Practice: Key Challenges and Lessons Learned
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    Chapter 26 Function Space Pooling for Graph Convolutional Networks
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    Chapter 27 Analysis of Optical Brain Signals Using Connectivity Graph Networks
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    Chapter 28 Property-Based Testing for Parameter Learning of Probabilistic Graphical Models
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    Chapter 29 An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge
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    Chapter 30 Inter-space Machine Learning in Smart Environments
Attention for Chapter 12: The European Legal Framework for Medical AI
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Chapter title
The European Legal Framework for Medical AI
Chapter number 12
Book title
Machine Learning and Knowledge Extraction
Published by
Springer, Cham, August 2020
DOI 10.1007/978-3-030-57321-8_12
Book ISBNs
978-3-03-057320-1, 978-3-03-057321-8
Authors

David Schneeberger, Karl Stöger, Andreas Holzinger, Schneeberger, David, Stöger, Karl, Holzinger, Andreas

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 44 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 44 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 20%
Student > Master 3 7%
Researcher 3 7%
Student > Doctoral Student 3 7%
Lecturer 2 5%
Other 9 20%
Unknown 15 34%
Readers by discipline Count As %
Computer Science 9 20%
Social Sciences 6 14%
Unspecified 2 5%
Biochemistry, Genetics and Molecular Biology 2 5%
Medicine and Dentistry 2 5%
Other 3 7%
Unknown 20 45%