Date of Submission


Date of Award


Institute Name (Publisher)

Indian Statistical Institute

Document Type

Doctoral Thesis

Degree Name

Doctor of Philosophy

Subject Name

Computer Science


Machine Intelligence Unit (MIU-Kolkata)


Pal, Sankar Kumar (MIU-Kolkata; ISI)

Abstract (Summary of the Work)

Real life problems are rarely free from uncertainty which usually emerges from the deficiencies of information available from a situation. The defi- ciencies may result from incomplete, imprecise, not fully reliable, vague or contradictory information depending on the problem. Management of uncer- tainty in a decision making system has been an important research problem for many years.Until the inception of the concept of fuzzy set theory in 1965 (1), the theory of probability and statistics was the primary mathematical tool for modeling uncertainty in a system/situation. Fuzzy set theory has shown enormous proinise in handling uncertaintics to a reasonable extent in various applications particularly in decision making models under different kinds of risks, subjective judgment, vagueness and ambiguity. This theory provides an approximate, yet effective and more flexible means of describing the be- havior of systems which are too complex or too ill-defined to admit precise mathematical analysis by classical methods and tools. Since this theory is a generalization of the classical set theory, it has greater flexibility to cap- ture various aspects of incompleteness or imperfection in information about a situation.Pattern recognition and machine learning form a major area of research and development activity that encompasses the processing of pictorial and other non-numerical information obtaincd from interaction between science, technology and society. The second motivation for this spurt of activity in this field is the need for the people to communicate with the computing machines in their natural mode of communication. The third and most important motivation is that the scientists are also concerned with the idea of designing and making automata that can carry out certain tasks as we human beings do. The most salient outcome of these is the concept of fifth generation computing systems.Machine recognition of patterns can be viewed as a two-fold task, con- sisting of learning the invariant properties of a set of samples characterizing a class, and of deciding that a new sample is a possible member of the class by noting that it has properties common to those of the set of samples. The tasks required for developing and implementing the decision rule can be described as a transformation from the measurement space M to the feature space F and finally to the decision space D, i.e.,M - F - D.In a pattern recognition system, the uncertainty can arise at any phase of the aforesaid tasks resulting from the incomplete or imprecise or ambiguous input information, the ill-defined and/or overlapping boundaries among the classes or regions, and the indefiniteness in defining/extracting features and relations among them. Any decision taken at a particular level will have an impact on all higher level activities. It is therefore required for a recogni- tion system to have sufficient provision for representing these uncertainties involved at every stage, so that the ultimate output (results) of the system can be obtained with least uncertainty (and not be affected or biased much by preceding decisions).


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Creative Commons Attribution 4.0 International License
This work is licensed under a Creative Commons Attribution 4.0 International License.


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