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

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9783319553115: Reverse Hypothesis Machine Learning: A Practitioner's Perspective (Intelligent Systems Reference Library, 128, Band 128)
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 is a marathon runner, Tedx speaker, husband of a bright doctor and above all a dreamer. He loves to write poetry and articulate his creative and innovative thoughts and deliver them through his passionate talks. He is also an entrepreneur, Machine Learning researcher and author of best-selling Innovation strategy, ML and Data science books. An avid reader, Parag holds Bachelors from Walchand College of Engineering Sangli (1990), PhD from IIT Kharagpur (2001), management education from IIM Kolkata and was conferred higher doctorate DSc by UGSM monarch, Switzerland (2010). He is the first higher doctorate in the area of knowledge innovation. Parag's machine learning ideas resulted in pioneering products and have become commercially successful and produced unprecedented impact. He delivered over one thousand keynote addresses and 200+ tutorials across the globe. Over 1000 institutes and 10,00,000+ professionals benefitted from Parag's talks, research and systemic consultations. Parag helped underperforming professionals, start-ups and students to transform into happy and passionate warriors. Fellow of the IET, IETE, and senior member IEEE, Parag is recipient of Oriental Foundation Scholarship, distinguished alumnus award WCE - Sangli and was nominated for prestigious Bhatnagar award in 2013 and 2014. He was also awarded IETE-KR Phadke award for innovative entrepreneurship and research in 2019. Parag has published over 300+ research papers and articles in peer reviewed journals and renown conferences. He invented over a dozen patents and authored 14 books (with world's best technical and business publishers like Bloomsbury, IEEE, Wiley, Prentice Hall, Springer, Oxford University Press, etc.). His book YD - YearDown portrays interesting perspective on education and was adapted for a TV serial by Sony TV by well-known movie director Sameer Patil. His poem collection was specially appreciated by one of the greatest romantic poets of alltimes late Mangesh Padgaonkar. Parag's book "Knowledge Innovation Strategy" was listed as a game changing business book by Hindustan Times. Niigata Times Japan mentioned that 'it is enlightening experience for readers' - It has foreword and endorsement with special acclamation by Dr. FC Kohli and Ratan Tata. Maharashtra Times, Hindustan Times, Times of India, Sakal, Pudhari from India and Niigata Times and Mainichi Newspaper from Japan published special articles highlighting Parag's contributions in democratization of AI and ML. Parag was the first PhD guide in Computer Engineering at COEP - Pune University (The second oldest technical institute in the country) and has guided 20 PhD candidates. He has over 30 years of experience in technologies, product building and applications of AI and ML to different verticals. In the past, he headed research divisions of many companies including Siemens (India & Germany), IDeaS (US and India) , ReasonEdge (Singapore) , Capsilon, etc. As an AI consultant he helped to build game changing products for companies like Envestnet, Tech Mahindra, UST Global, Agrisk, Tata Consumers etc. He founded start-ups iKnowlation - India, Kvinna Limited New Zealand and created social value through innovation and research. Parag is a prolific speaker and is associated with many technical and B-schools of repute like IITs, IIMs, Tokyo Int. University Japan and Masaryk University, Brno - Czech Republic etc. He has been taking special efforts and working closely with remote technical institutes in Maharashtra to inculcate research, thinking and entrepreneurship skills among students, faculties and researchers. He is a pioneer of concepts of Systemic Machine Learning, Reverse Hypothesis Machine Learning, Context Vector Machines and Choice computing. He has helped as AI and ML consultant and innovation strategist to over two dozen organizations in Singapore, US, Japan and India and contributed to their success stories. He worked on socialgood and developed over a dozen p

Von der hinteren Coverseite

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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Verlag
Springer
Erscheinungsdatum
2017
Sprache
Englisch
ISBN 10 
3319553119
ISBN 13 
9783319553115
Einband
Gebundene Ausgabe
Auflage
1
Anzahl der Seiten
156
Kontakt zum Hersteller
ProductSafety@springernature.com
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Darmstadt
64293
Deutschland

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