 Название: Reverse Hypothesis Machine Learning: A Practitioner's Perspective
Автор: Parag Kulkarni
Издательство: Springer
Год: 2017
Страниц: 150
Язык: английский
Формат: pdf (true), epub
Размер: 10.1 MB
This book introduces a paradigm of reverse hypothesis machines (RHM), focusing on knowledge innovation and Machine Learning (ML). 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.
Machine Learning is art and science, it is thinking and application and it is psychology and mathematics. Hardly any other field has a mix of so many wonderful areas of science and technology combined. This book takes a fresh look at this vibrant area from the perspective of knowledge innovation. Knowledge innovation is beyond knowledge acquisition, it is optimally handling limited data and it is coming up with surprises through ability to innovate already acquired knowledge. I think this machine learning journey on knowledge innovation wheels bathed in a fresh perspective will bring delight to readers, researchers, and ML professionals. Every professional who directly or indirectly related to machine learning will find something interesting from this book to march toward pinnacle of his/her ML career. So tighten your seat belt to take off to creative machine learning journey…
Contents:
Part I Building Foundation: Decoding Knowledge Acquisition
1 Introduction: Patterns Apart 3
2 Understanding Machine Learning Opportunities 23
3 Systemic Machine Learning 49
4 Reinforcement and Deep Reinforcement Machine Learning 59
Part II Learnability Route: Reverse Hypothesis Machines
5 Creative Machine Learning 87
6 Co-operative and Collective Learning for Creative ML 119
7 Building Creative Machines with Optimal ML and Creative Machine Learning Applications 125
8 Conclusion—Learning Continues 133
Bibliography 135
Index 137
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