Explainable and Interpretable Models in Computer Vision and Machine Learning
158,72 €*
Alle Preise inkl. MwSt.|Versandkostenfrei
ISBN-13:
9783319981307
Veröffentl:
2019
Einband:
Book + eBook
Erscheinungsdatum:
01.09.2018
Seiten:
299
Autor:
Hugo Jair Escalante
Gewicht:
673 g
Format:
241x161x22 mm
Serie:
The Springer Series on Challenges in Machine Learning
Sprache:
Englisch
Beschreibung:
This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.
Presents a snapshot of explainable and interpretable models in the context of computer vision and 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.
Kunden Rezensionen
Mehr zum Thema
Folgen Sie uns auf:
