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Reverse Hypothesis Machine Learning A Practitioners Persp

 
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MessagePosté le: Jeu 6 Juil - 06:30 (2017)    Sujet du message: Reverse Hypothesis Machine Learning A Practitioners Persp Répondre en citant




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.



Details:
rank:
price: $80.78
bound:
publisher: Springer; 1st ed. 2017 edition (March 30, 2017)
lang: English
asin:
isbn: 3319553119, 978-3319553115,
weight: 15.5 ounces (
filesize:






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It can also used by practitioners to develop new machine Mediterrane Küche: Leckere Rezepte, die nach Urlaub schmecken! (German Edition) applications to solve How to Design and Write Web Pages Today, 2nd Edition that require creativityIntelligent Systems Reference Library Free Preview 2017 Reverse Hypothesis Machine Learning A Practitioner's Perspective Enterprise Security: Second International Workshop, ES 2015, Vancouver, BC, Canada, November 30 – December 3, 2015, Revised Selected Papers (Lecture Notes in Computer Science) Kulkarni, Parag Presents a practitioners perspective on knowledge innovation and machine learning Discusses different aspects of knowledge innovation applied to systemic machine learning paradigms Includes case studies on building creative machines including learning components for various real life problems see more benefits About this book 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 consumingThe book is useful as a reference book for machine learning researchers and professionals as well as Construction Laborer Log: Logbook, Journal - 102 pages, 5 x 8 inches (Unique Logbooks/Record Books) intelligence enthusiastsHence Knowledge innovation based learning is the need of timeTable of contents (8 chapters) Introduction: Patterns Apart Kulkarni, Parag Pages 3-22 Preview Buy Chapter .95 Understanding Machine Learning Opportunities Kulkarni, Parag Pages 23-48 Preview Buy Chapter .95 Systemic Machine Learning Kulkarni, Parag Pages 49-58 Preview Buy Chapter .95 Reinforcement and Deep Reinforcement Machine Learning Kulkarni, Parag Pages 59-83 Preview Buy Chapter .95 Creative Machine Learning Steam Generators and Waste Heat Boilers: For Process and Plant Engineers (Mechanical Engineering) Parag Pages 87-118 Preview Buy Chapter .95 Co-operative and Collective Learning for Creative ML Kulkarni, Parag Pages 119-124 Preview Buy Chapter .95 Building Creative Machines with Optimal ML and Creative Machine Learning Applications Kulkarni, Parag Pages 125-132 Preview Buy Chapter .95 ConclusionLearning Continues Kulkarni, Parag Pages 133-134 Preview Buy Chapter .95 Show next xx It was written as a training companion and as a must-read, not only for physicians and students, but also for any one involved in the process and progress of health a Rendering the action space onto the cortex therefore results in a complex pattern, explaining the otherwise inexplicable details of the motor cortex organization

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