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OverviewThis 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. Full Product DetailsAuthor: Parag KulkarniPublisher: Springer International Publishing AG Imprint: Springer International Publishing AG Edition: Softcover reprint of the original 1st ed. 2017 Volume: 128 Weight: 2.467kg ISBN: 9783319856261ISBN 10: 331985626 Pages: 138 Publication Date: 25 July 2018 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsPattern Apart.- Understanding Machine Learning Opportunities.- Systemic Machine Learning.- Reinforcement and Deep Reinforcement Machine Learning.- Creative Machine Learning.- Co-operative and Collective learning for Creative Machine Learning.- Building Creative Machines with Optimal Machine Learning and Creative Machine Learning Applications.- Conclusion – Learning ContinuesReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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