Reverse Hypothesis Machine Learning : A Practitioner's Perspective
Language: English
Published by Birkhäuser, 2018
Series: Book 97 of 188 - Intelligent Systems Reference Library
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
£ 127.54
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Add to basketItem description from seller
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.…
Seller Inventory # 9783319856261
- Title
- Reverse Hypothesis Machine Learning : A Practitioner's Perspective
- Author
- Parag Kulkarni
- Publisher
- Birkhäuser
- Publication year
- 2018
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 331985626X
- ISBN 13
- 9783319856261
- Item weight
- 248 grams
- Dimensions
- 235x155x9 mm
- Series
- Book 97 of 188: Intelligent Systems Reference Library
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.
"Synopsis" may belong to another edition of this title.
From the Back Cover
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.
"About the title" may belong to another edition of this title.
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