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Explainable and Interpretable Models in Computer Vision and Machine Learning (The Springer Series on Challenges in Machine Learning) - Hardcover
Synopsis
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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- PublisherSpringer
- Publication date2019
- ISBN 10 3319981307
- ISBN 13 9783319981307
- BindingHardcover
- LanguageEnglish
- Number of pages316
- EditorEscalante Hugo Jair, Escalera Sergio, Guyon Isabelle, Baró Xavier, Güçlütürk Yağmur, Güçlü Umut, Van Gerven Marcel
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Explainable and Interpretable Models in Computer Vision and Machine Learning.
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XVII, 299 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. The Springer Series on Challenges in Machine Learning. Sprache: Englisch. Seller Inventory # 34532AB
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Explainable and Interpretable Models in Computer Vision and Machine Learning
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Springer International Publishing, 2019
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Seller: moluna, Greven, Germany
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Condition: New. Presents a snapshot of explainable and interpretable models in the context of computer vision and machine learningCovers fundamental topics to serve as a reference for newcomers to the fieldOffers successful methodologies, with appli. Seller Inventory # 266307510
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Explainable and Interpretable Models in Computer Vision and Machine Learning
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Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Taschenbuch
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.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 299 pp. Englisch. Seller Inventory # 9783319981307
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Published by
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
New
Taschenbuch
Seller: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.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 299 pp. Englisch. Seller Inventory # 9783319981307
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Published by
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Bündel. Condition: Neu. 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
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Explainable and Interpretable Models in Computer Vision and Machine Learning (Book & Merchandise)
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Springer International Publishing AG, Cham, 2019
ISBN 10: 3319981307
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Seller: Grand Eagle Retail, Fairfield, OH, U.S.A.
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Book & Merchandise. Condition: new. Book & Merchandise. 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 Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9783319981307
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Published by
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
New
Taschenbuch
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. 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.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. Seller Inventory # 9783319981307
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Explainable and Interpretable Models in Computer Vision and Machine Learning
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Springer-Verlag New York Inc, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Paperback. Condition: Brand New. pap/psc edition. 299 pages. 9.25x6.10x0.79 inches. In Stock. Seller Inventory # x-3319981307
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