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Nonparametric Bayesian Learning Collaborative by Zhou Xuefeng (27 results)
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811562652 ISBN 13: 9789811562655
Language: English
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Add to basketTaschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book focuses onrobot introspection,whichhas a direct impact on physical human-robot interactionandlong-term autonomy,andwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,the abilitytoreason,solve their ownanomaliesand proactivelyenrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which caneffectivelybe modeled as a parametrichidden Markovmodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using thehierarchical Dirichletprocess (HDP) on the standard HMM parameters,known as theHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is avaluablereferenceresource forresearchers and designers inthe fieldof robot learning and multimodal perception, as well as for senior undergraduate and graduateuniversitystudents.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore, Springer Nature Singapore, 2020
ISBN 10: 9811562628 ISBN 13: 9789811562624
Language: English
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Add to basketBuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This open access book focuses onrobot introspection,whichhas a direct impact on physical human-robot interactionandlong-term autonomy,andwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,the abilitytoreason,solve their ownanomaliesand proactivelyenrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which caneffectivelybe modeled as a parametrichidden Markovmodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using thehierarchical Dirichletprocess (HDP) on the standard HMM parameters,known as theHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is avaluablereferenceresource forresearchers and designers inthe fieldof robot learning and multimodal perception, as well as for senior undergraduate and graduateuniversitystudents.
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Add to basketHardcover. Condition: Brand New. 154 pages. 9.25x6.10x0.44 inches. In Stock.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore, Springer Nature Singapore Sep 2020, 2020
ISBN 10: 9811562652 ISBN 13: 9789811562655
Language: English
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Add to basketTaschenbuch. Condition: Neu. Neuware -This open access book focuses on robot introspection, which has a direct impact on physical human¿robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore, Springer Nature Singapore Jul 2020, 2020
ISBN 10: 9811562628 ISBN 13: 9789811562624
Language: English
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
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Add to basketBuch. Condition: Neu. Neuware -This open access book focuses on robot introspection, which has a direct impact on physical human¿robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 156 pp. Englisch.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore Sep 2020, 2020
ISBN 10: 9811562652 ISBN 13: 9789811562655
Language: English
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Add to basketTaschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book focuses onrobot introspection,whichhas a direct impact on physical human-robot interactionandlong-term autonomy,andwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,the abilitytoreason,solve their ownanomaliesand proactivelyenrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which caneffectivelybe modeled as a parametrichidden Markovmodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using thehierarchical Dirichletprocess (HDP) on the standard HMM parameters,known as theHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is avaluablereferenceresource forresearchers and designers inthe fieldof robot learning and multimodal perception, as well as for senior undergraduate and graduateuniversitystudents. 156 pp. Englisch.
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Add to basketCondition: New. Print on Demand pp. XVII, 137 50 illus., 44 illus. in color.
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Add to basketCondition: New. PRINT ON DEMAND pp. XVII, 137 50 illus., 44 illus. in color.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore Jul 2020, 2020
ISBN 10: 9811562628 ISBN 13: 9789811562624
Language: English
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
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Add to basketBuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This open access book focuses onrobot introspection,whichhas a direct impact on physical human-robot interactionandlong-term autonomy,andwhich can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics,the abilitytoreason,solve their ownanomaliesand proactivelyenrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which caneffectivelybe modeled as a parametrichidden Markovmodel (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using thehierarchical Dirichletprocess (HDP) on the standard HMM parameters,known as theHierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states andallows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods.This book is avaluablereferenceresource forresearchers and designers inthe fieldof robot learning and multimodal perception, as well as for senior undergraduate and graduateuniversitystudents. 156 pp. Englisch.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore, 2020
ISBN 10: 9811562652 ISBN 13: 9789811562655
Language: English
US$ 47.95
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Add to basketKartoniert / Broschiert. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Is the first book on robot introspection based on nonparametric Bayesian methods in a data-driven context, which can be easily integrated into various robotic systemsIntroduces a fast, accurate, robot anomaly monitoring, diagnosis and&nb.
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Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection
Published by Springer Nature Singapore, 2020
ISBN 10: 9811562628 ISBN 13: 9789811562624
Language: English
US$ 58.57
Convert currencyUS$ 57.59 shipping from Germany to U.S.A.Quantity: Over 20 available
Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Is the first book on robot introspection based on nonparametric Bayesian methods in a data-driven context, which can be easily integrated into various robotic systemsIntroduces a fast, accurate, robot anomaly monitoring, diagnosis and&nb.
