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Explainable and interpretable models...

Escalante, Hugo Jair.

Explainable and interpretable models in computer vision and machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Explainable and interpretable models in computer vision and machine learningedited by Hugo Jair Escalante ... [et al.].
其他作者:	Escalante, Hugo Jair.
出版者:	Cham :Springer International Publishing :2018.
面頁冊數:	xvii, 299 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	https://doi.org/10.1007/978-3-319-98131-4
ISBN:	9783319981314$q(electronic bk.)

Explainable and interpretable models in computer vision and machine learning
Explainable and interpretable models in computer vision and machine learning[electronic resource] /edited by Hugo Jair Escalante ... [et al.]. - Cham :Springer International Publishing :2018. - xvii, 299 p. :ill. (some col.), digital ;24 cm. - The springer series on challenges in machine learning,2520-131X. - Springer series on challenges in machine learning..

1 Considerations for Evaluation and Generalization in Interpretable Machine Learning -- 2 Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges -- 3 Learning Functional Causal Models with Generative Neural Networks -- 4 Learning Interpretable Rules for Multi-label Classification -- 5 Structuring Neural Networks for More Explainable Predictions -- 6 Generating Post-Hoc Rationales of Deep Visual Classification Decisions -- 7 Ensembling Visual Explanations -- 8 Explainable Deep Driving by Visualizing Causal Action -- 9 Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening -- 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions -- 11 On the Inherent Explainability of Pattern Theory-based Video Event Interpretations.

ISBN: 9783319981314$q(electronic bk.)

Standard No.: 10.1007/978-3-319-98131-4doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

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505 0 $a 1 Considerations for Evaluation and Generalization in Interpretable Machine Learning -- 2 Explanation Methods in Deep Learning: Users, Values, Concerns and Challenges -- 3 Learning Functional Causal Models with Generative Neural Networks -- 4 Learning Interpretable Rules for Multi-label Classification -- 5 Structuring Neural Networks for More Explainable Predictions -- 6 Generating Post-Hoc Rationales of Deep Visual Classification Decisions -- 7 Ensembling Visual Explanations -- 8 Explainable Deep Driving by Visualizing Causal Action -- 9 Psychology Meets Machine Learning: Interdisciplinary Perspectives on Algorithmic Job Candidate Screening -- 10 Multimodal Personality Trait Analysis for Explainable Modeling of Job Interview Decisions -- 11 On the Inherent Explainability of Pattern Theory-based Video Event Interpretations.
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