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

Author:   Andreas Holzinger ,  Peter Kieseberg ,  A Min Tjoa ,  Edgar Weippl
Publisher:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12279
ISBN:  

9783030573201


Pages:   552
Publication Date:   21 August 2020
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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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


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Overview

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.

Full Product Details

Author:   Andreas Holzinger ,  Peter Kieseberg ,  A Min Tjoa ,  Edgar Weippl
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
Edition:   1st ed. 2020
Volume:   12279
Weight:   0.854kg
ISBN:  

9783030573201


ISBN 10:   3030573206
Pages:   552
Publication Date:   21 August 2020
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of 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: Impactof 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.

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