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Explainable and interpretable models...

Escalante, Hugo Jair.

Explainable and interpretable models in computer vision and machine learning

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Explainable and interpretable models in computer vision and machine learning/ edited by Hugo Jair Escalante ... [et al.].
other author:	Escalante, Hugo Jair.
Published:	Cham :Springer International Publishing : : 2018.,
Description:	xvii, 299 p. :ill. (some col.), digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	https://doi.org/10.1007/978-3-319-98131-4
ISBN:	9783319981314

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

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

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Explainable and interpretable models in computer vision and machine learning
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020 $a 9783319981307 $q (paper)
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245 0 0 $a Explainable and interpretable models in computer vision and machine learning $h [electronic resource] / $c edited by Hugo Jair Escalante ... [et al.].
260 $a Cham : $c 2018. $b Springer International Publishing : $b Imprint: Springer,
300 $a xvii, 299 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a The springer series on challenges in machine learning, $x 2520-131X
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.
650 0 $a Machine learning. $3 561253
650 0 $a Computer vision. $3 561800
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Image Processing and Computer Vision. $3 670819
650 2 4 $a Pattern Recognition. $3 669796
700 1 $a Escalante, Hugo Jair. $3 1211071
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
830 0 $a Springer series on challenges in machine learning. $3 1143127
856 4 0 $u https://doi.org/10.1007/978-3-319-98131-4
950 $a Computer Science (Springer-11645)

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