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Soft Computing in Machine Learning


  • ISBN:9781642241716
  • Contributors: Uthai Phommasak, Mita K. Dalal
  • Format: Hardcover
  • Year: 2019
  • Pages: 303
  • Availability: In Stock

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Soft Computing (SC) represents a significant paradigm shift in the aims of computing, which reflects the fact that the human mind, unlike present day computers, possesses a remarkable ability to store and process information which is pervasively imprecise, uncertain and lacking in categoricity. Soft Computing (SC) is an emerging field that consists of complementary elements of fuzzy logic, neural computing, evolutionary computation, machine learning and probabilistic reasoning. Due to their strong learning and cognitive ability and good tolerance of uncertainty and imprecision, soft computing techniques have found wide applications. The volume of data being produced is increasing at an exponential rate due to our unprecedented capacity to generate, capture and share vast amounts of data. In this context, Machine Learning (ML) algorithms can be used to extract information from these large volumes of data. However, these algorithms are computationally expensive.

Soft Computing in Machine Learning deals with cutting-edge coverage dealing with an extensive volume of data, to teach machines and to improve decision making models. Hence, ML algorithms often demand prohibitive computational resources when facing large volumes of data. Therefore, the scientific breakthroughs of the future will undoubtedly be powered by advanced computing capabilities that will allow researchers to manipulate and explore massive datasets.

This book aims to provide investigators in the areas of information systems, engineering, computer science, statistics and management, with an insightful source for the role of soft computing in machine learning. As well, social sciences, psychology, medicine, genetics, and other fields that are interested in solving intricate problems can much benefit from this book.

The book can also serve as a beneficial tool for advanced undergraduate students in data mining and machine learning. It may be particularly interested for practitioners and researchers in the descriptions of real-world data mining projects performed with soft-computing.