Explainable interpretable models computer (15 risultati)
Lingua: Inglese
Editore: Cham, Springer., 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: Universitätsbuchhandlung Herta Hold GmbH, Berlin, GermaniaUniversitätsbuchhandlung Herta Hold GmbH
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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: Englis…ch.
Lingua: Inglese
Editore: Springer, 2019
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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Gebundene Ausgabe. Condizione: 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.
Lingua: Inglese
Editore: Springer International Publishing, 2019
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: moluna, Greven, Germaniamoluna
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Condizione: 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.
Lingua: Inglese
Editore: Springer-Verlag GmbH, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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UNK. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Springer-Verlag GmbH, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: Buchpark, Trebbin, GermaniaBuchpark
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Condizione: Hervorragend. Zustand: Hervorragend | Seiten: 299 | Sprache: Englisch | Produktart: Bücher | 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 recognit…ion 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Sep 2018, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 160,49
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Taschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Sep 2018, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
- Brossura
Da: Rheinberg-Buch Andreas Meier eK, Bergisch Gladbach, GermaniaRheinberg-Buch Andreas Meier eK
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EUR 160,49
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Taschenbuch. Condizione: 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Sep 2018, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
- Rilegato
Da: Wegmann1855, Zwiesel, GermaniaWegmann1855
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Bündel. Condizione: 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.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 177,14
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Paperback. Condizione: Brand New. pap/psc edition. 299 pages. 9.25x6.10x0.79 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing AG, Cham, 2019
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
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Book & Merchandise. Condizione: 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.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2019
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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Mixed Media Product. Condizione: New. 2018 ed. 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 almos…t 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.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Sep 2018, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
- Rilegato
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 160,49
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Bündel. Condizione: 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.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 299 pp. Englisch.
Lingua: Inglese
Editore: Springer-Verlag Gmbh Sep 2018, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
- Rilegato
Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
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Kombiprodukt. Condizione: 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 huma…n-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.
Lingua: Inglese
Editore: Springer-Verlag New York Inc, 2018
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
- Brossura
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 236,97
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Paperback. Condizione: Brand New. pap/psc edition. 299 pages. 9.25x6.10x0.79 inches. In Stock.
Lingua: Inglese
Editore: Springer International Publishing AG, CH, 2019
Serie: Libro 4 di 8 - The Springer Series on Challenges in Machine Learning
- Rilegato
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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EUR 182,82
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Mixed Media Product. Condizione: New. 2018 ed. 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 almos…t 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.
