Reverse Hypothesis Machine Learning: A Practitioner's Perspective (Intelligent Systems Reference Library, 128, Band 128) - Hardcover

Kulkarni

Hardcover

ISBN 10: 3319553119 ISBN 13: 9783319553115

Verlag: Springer, 2017

Inhaltsangabe

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.

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Über die Autorin bzw. den Autor

Parag (PhD, DSc) is an entrepreneur, AI/ML researcher, professor at Tokyo International University and author of best-selling innovation strategy and AI books. He holds Ph.D. from IIT Kharagpur, Management Education from IIM Kolkata, and was conferred higher doctorate D.Sc. by UGSM Monarch Business School, Switzerland. Parag’s AI ideas resulted in commercially successful pioneering products to deliver unprecedented value. He contributed to the success of over two dozen startups through his research and paradigm shifting strategies. Parag headed research labs of many companies including IDeaS, Capsilon, SAC Siemens, etc and was the research brain behind two 100+ million dollar acquisition success stories. Founder, chief scientist and advisor of iKnowlation Research Lab, Parag exited two startups successfully. Parag has published over 300+ research papers, invented over a dozen patents and authored 16 books. His book "Knowledge Innovation Strategy" was listed as a game-changing business book by Hindustan Times. Fellow of IETE and The IET, Parag was the first Ph.D. guide in Computer Engineering at COEP Technological University Pune and has guided 20 Ph.D. candidates.

Dr. Jay Rajasekera’s specialties are in digital business and computer algorithms. He is currently the vice president and dean of Graduate School of Business and Commerce at Tokyo International University. He started his career at AT&T Bell Laboratories in New Jersey, USA, following his Ph.D. in Operations Research and Computer Science. While at Bell Laboratories he designed and patented the computer algorithm for the construction of the world's first undersea fiber optic cable TAT-8 between USA and Europe; world’s first demonstrations of the Internet too were done using this cable. From 1995 to 2017, he was with the International University of Japan, holding various positions. In 2018, he moved to Tokyo International University and contributed to establish new Digital Business and Innovation (DBI) program. In 2023, he and his team won a global AI competition, which led to applying AI for issuing cyber alerts for one of top global airports. Companies he had offered consulting include IBM, Fujitsu and Hitachi.

Prof. Chaudhuri retired from Indian Statistical Institute, Kolkata, India as INAE Distinguished Professor and J C Bose Fellow in 2018 and acted as Pro VC of Techno India University for five years. Currently, he is a distinguished Emeritus pProfessor at Techno India University and visiting professor of several other Universities. His research areas of interest are AI and machine learning in computer sciences, especially in pattern recognition, computer vision, natural language processing and their applications in various fields. He published more than 450 papers, authored or edited 10 books in these domains. He also authored several patents and acted as chief investigator of externally funded projects like UNDP, Indo-European, Indo-German and Indo-France and many domestic projects. He is a life fellow of IEEE and fellow of IAPR, AAIA, TWAS, INSA, INAE, INASc, IETE, etc.

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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 zur Ausgabe

• Verlag: Springer
• Erscheinungsdatum: 2017
• Sprache: Englisch
• ISBN 10: 3319553119
• ISBN 13: 9783319553115
• Einband: Gebundene Ausgabe
• Auflage: 1
• Anzahl der Seiten: 156
• Kontakt zum Hersteller: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3; Heidelberg; 69115; Deutschland