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Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
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Explainable and Interpretable Models in Computer Vision and Machine Learning (Book, 1st ed. 2018)
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Explainable and Interpretable Models in Computer Vision and Machine Learning (Book, 1st ed. 2018)
Series: The Springer Series on Challenges in Machine Learning
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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
General
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