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Lingua: Inglese
Editore: Springer International Publishing, 2018
ISBN 10: 331985626X ISBN 13: 9783319856261
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Condizione: New. Softcover reprint of the original 1st ed. 2017 edition NO-PA16APR2015-KAP.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Reverse Hypothesis Machine Learning | A Practitioner's Perspective | Parag Kulkarni | Taschenbuch | Intelligent Systems Reference Library | xvi | Englisch | 2018 | Springer | EAN 9783319856261 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
Lingua: Inglese
Editore: Springer International Publishing, Springer International Publishing, 2018
ISBN 10: 331985626X ISBN 13: 9783319856261
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning.Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning.All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same-the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learningapplications to solve problems that require creativity.
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Lingua: Inglese
Editore: Springer International Publishing Jul 2018, 2018
ISBN 10: 331985626X ISBN 13: 9783319856261
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning.Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning.All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same-the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learningapplications to solve problems that require creativity. 156 pp. Englisch.
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND.
Lingua: Inglese
Editore: Springer, Palgrave Macmillan Jul 2018, 2018
ISBN 10: 331985626X ISBN 13: 9783319856261
Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
EUR 128,39
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and machine learning. Knowledge- acquisition -based learning is constrained by large volumes of data and is time consuming. Hence Knowledge innovation based learning is the need of time. Since under-learning results in cognitive inabilities and over-learning compromises freedom, there is need for optimal machine learning. All existing learning techniques rely on mapping input and output and establishing mathematical relationships between them. Though methods change the paradigm remains the same-the forward hypothesis machine paradigm, which tries to minimize uncertainty. The RHM, on the other hand, makes use of uncertainty for creative learning. The approach uses limited data to help identify new and surprising solutions. It focuses on improving learnability, unlike traditional approaches, which focus on accuracy. The book is useful as a reference book for machine learning researchers and professionals as well as machine intelligence enthusiasts. It can also used by practitioners to develop new machine learning applications to solve problems that require creativity.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 156 pp. Englisch.