Explainable and Interpretable Models in Computer Vision and Machine Learning
Language: English
Published by Springer-Verlag Gmbh Sep 2018, 2018
Series: Book 4 of 8 - The Springer Series on Challenges in Machine Learning
- Hardcover
- New
Seller: Wegmann1855, Zwiesel, GermanyWegmann1855
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Neuware -This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Seller Inventory # 9783319981307
- Title
- Explainable and Interpretable Models in Computer Vision and Machine Learning
- Author
- Hugo Jair Escalante
- Publisher
- Springer-Verlag Gmbh Sep 2018
- Publication year
- 2018
- Condition
- Neu
- Binding
- Bündel
- Language
- English
- ISBN 10
- 3319981307
- ISBN 13
- 9783319981307
- Item weight
- 673 grams
- Dimensions
- 241x161x22 mm
- Series
- Book 4 of 8: The Springer Series on Challenges in Machine Learning
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
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About the Author
"About the title" may belong to another edition of this title.
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