Reverse Hypothesis Machine Learning

A Practitioner's Perspective
1. Auflage 2017
ISBN: 978-3-319-55311-5
Verlag: Springer International Publishing

Buch, Englisch, Band 128, 138 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 3613 g

Reihe: 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.

Zielgruppe

Research

Autoren/Hrsg.

Kulkarni, Parag

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
• Wirtschaftswissenschaften Betriebswirtschaft Management Wissensmanagement
• Wirtschaftswissenschaften Betriebswirtschaft Management Forschung & Entwicklung (F&E), Innovation
• Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Maschinenbau Triebwerkstechnik, Energieübertragung

Inhaltsverzeichnis

Pattern 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 Continues