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The Springer Series on Challenges in Machine Learning

Explainable and Interpretable Models in Computer Vision and Machine Learning

Editors: Jair Escalante, H., Escalera, S., Guyon, I., Baró, X., Güçlütürk, Y., Güçlü, U., van Gerven, M.A.J. (Eds.)

  • Presents a snapshot of explainable and interpretable models in the context of computer vision and machine learning
  • Covers fundamental topics to serve as a reference for newcomers to the field
  • Offers successful methodologies, with applications of interest to the machine learning and computer vision communities
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eBook $89.00
price for USA in USD (gross)
  • ISBN 978-3-319-98131-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover + eBook $119.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: September 24, 2018
  • ISBN 978-3-319-98130-7
  • Immediate eBook download after purchase
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Free shipping for individuals worldwide
About this book

This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.

Research progress in computer vision and pattern recognition has led to a variety of modeling techniques with almost human-like performance. Although these models have obtained astounding results, they are limited in their explainability and interpretability: what is the rationale behind the decision made? what in the model structure explains its functioning? Hence, while good performance is a critical required characteristic for learning machines, explainability and interpretability capabilities are needed to take learning machines to the next step to include them in decision support systems involving human supervision.   

 This book, written by leading international researchers, addresses key topics of explainability and interpretability, including the following:

 

·         Evaluation and Generalization in Interpretable Machine Learning

·         Explanation Methods in Deep Learning

·         Learning Functional Causal Models with Generative Neural Networks

·         Learning Interpreatable Rules for Multi-Label Classification

·         Structuring Neural Networks for More Explainable Predictions

·         Generating Post Hoc Rationales of Deep Visual Classification Decisions

·         Ensembling Visual Explanations

·         Explainable Deep Driving by Visualizing Causal Attention

·         Interdisciplinary Perspective on Algorithmic Job Candidate Search

·         Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions

·         Inherent Explainability Pattern Theory-based Video Event Interpretations


Table of contents (11 chapters)

  • Considerations for Evaluation and Generalization in Interpretable Machine Learning

    Doshi-Velez, Finale (et al.)

    Pages 3-17

  • Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges

    Ras, Gabriëlle (et al.)

    Pages 19-36

  • Learning Functional Causal Models with Generative Neural Networks

    Goudet, Olivier (et al.)

    Pages 39-80

  • Learning Interpretable Rules for Multi-Label Classification

    Mencía, Eneldo Loza (et al.)

    Pages 81-113

  • Structuring Neural Networks for More Explainable Predictions

    Rieger, Laura (et al.)

    Pages 115-131

Buy this book

eBook $89.00
price for USA in USD (gross)
  • ISBN 978-3-319-98131-4
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover + eBook $119.99
price for USA in USD
  • Customers within the U.S. and Canada please contact Customer Service at 1-800-777-4643, Latin America please contact us at +1-212-460-1500 (Weekdays 8:30am – 5:30pm ET) to place your order.
  • Due: September 24, 2018
  • ISBN 978-3-319-98130-7
  • Immediate eBook download after purchase
  • Digitally watermarked, DRM-free
  • Included format: PDF, EPUB
  • ebooks can be used on all reading devices
  • Free shipping for individuals worldwide
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Bibliographic Information

Bibliographic Information
Book Title
Explainable and Interpretable Models in Computer Vision and Machine Learning
Editors
  • Hugo Jair Escalante
  • Sergio Escalera
  • Isabelle Guyon
  • Xavier Baró
  • Yağmur Güçlütürk
  • Umut Güçlü
  • Marcel A. J. van Gerven
Series Title
The Springer Series on Challenges in Machine Learning
Copyright
2018
Publisher
Springer International Publishing
Copyright Holder
Springer Nature Switzerland AG
eBook ISBN
978-3-319-98131-4
DOI
10.1007/978-3-319-98131-4
Hardcover ISBN
978-3-319-98130-7
Series ISSN
2520-131X
Edition Number
1
Number of Pages
XVII, 299
Number of Illustrations
15 b/w illustrations, 58 illustrations in colour
Topics