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Explainable and Interpretable Models in Computer Vision and Machine Learning (The Springer Series on Challenges in Machine Learning) - Tapa dura
Sinopsis
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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Explainable and Interpretable Models in Computer Vision and Machine Learning.
Publicado por
Cham, Springer., 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, Alemania
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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. Nº de ref. del artículo: 34532AB
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer-Verlag GmbH, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Librería: Buchpark, Trebbin, Alemania
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Condición: Hervorragend. Zustand: Hervorragend | Seiten: 299 | Sprache: Englisch | Produktart: Bücher. Nº de ref. del artículo: 29483391/1
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Explainable and Interpretable Models in Computer Vision and Machine Learning (The Springer Series on Challenges in Machine Learning)
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
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Gebundene Ausgabe. Condición: Sehr gut. Gebraucht - Sehr gut - ungelesen,als Mängelexemplar gekennzeichnet, mit leichten Mängeln an Schnitt oder Einband durch Lager- oder Transportschaden -This book compiles leading research on the development of explainable and interpretable machine learning methods in the context of computer vision and machine learning.Springer Fachmedien Wiesbaden GmbH, Abraham-Lincoln-Str. 46, 65189 Wiesbaden 316 pp. Englisch. Nº de ref. del artículo: INF1000251058
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer International Publishing, 2019
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Librería: moluna, Greven, Alemania
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Condición: 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. Nº de ref. del artículo: 266307510
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Taschenbuch
Librería: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783319981307
Cantidad disponible: 1 disponibles
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
Nuevo
Taschenbuch
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783319981307
Cantidad disponible: 1 disponibles
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
Nuevo
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Librería: Wegmann1855, Zwiesel, Alemania
Calificación del vendedor: 2 de 5 estrellas
Bündel. Condición: 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. Nº de ref. del artículo: 9783319981307
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer-Verlag Gmbh Sep 2018, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: 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. Nº de ref. del artículo: 9783319981307
Cantidad disponible: 1 disponibles
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Explainable and Interpretable Models in Computer Vision and Machine Learning
Publicado por
Springer-Verlag New York Inc, 2018
ISBN 10: 3319981307
ISBN 13: 9783319981307
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Paperback
Librería: Revaluation Books, Exeter, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Paperback. Condición: Brand New. pap/psc edition. 299 pages. 9.25x6.10x0.79 inches. In Stock. Nº de ref. del artículo: x-3319981307
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Explainable and Interpretable Models in Computer Vision and Machine Learning (Book & Merchandise)
Publicado por
Springer International Publishing AG, Cham, 2019
ISBN 10: 3319981307
ISBN 13: 9783319981307
Nuevo
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Librería: Grand Eagle Retail, Fairfield, OH, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Book & Merchandise. Condición: 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. Nº de ref. del artículo: 9783319981307
Cantidad disponible: 1 disponibles