Author Description

During the past decade there has been a considerable growth of interest in problems of pattern recognition and machine learning. This interest has created an increasing need for methods and techniques for the design of pattern recognition and learning systems. Many different approaches have been proposed. One of the most promising techniques for the solution of problems in pattern recognition and machine learning is the statistical theory of decision and estimation. This monograph treats the problems of pattern recognition and machine learning by use of sequential methods in statistical decision and estimation. In presenting the material, emphasis is placed upon the development of basic theory and computation algorithms in systematic fashion. The monograph is intended to be of use both as a reference for system engineers and computer scientists and as a supplementary textbook for courses in pattern recognition and adaptive and learning systems.