Innovations in Machine Learning: Theory and Applications (Studies in Fuzziness and Soft Computing) : 9783540306092

The study of innovation – the development of new knowledge and artifacts – is of interest to scientists and practitioners. Innovations change the day-to-day lives of individuals, transform economies, and even create new societies. The conditions triggering innovation and the innovation process itself set the stage for economic growth.

Scholars of technology have indicated that innovation lies at the intersection of science and technology. One view proposes that innovation is possible through advances in basic science and is realized in concrete products within the context of applied science. Another view states that the development of innovative products through applied science generates new resources on which basic science draws to advance new ideas and theories. Some believe that that science and technology form a symbiotic relationship, drawing from and contributing to one another's progress. Following this view, innovation in any domain can be enhanced by principles and insights from diverse disciplines.

This book addresses an important component of innovation dealing with knowledge discovery. The discovery aspect creates a natural bridge between machine learning concepts, models, and algorithms and innovation. In years to come machine learning will mark some of the early fundamentals leading to innovative science.