Machine learning and expert utility assessment

Pavlov, Yuri; Ljakova, K.

doi:10.1145/973620.973693

Proceedings of the 4th International Conference Conference on Computer Systems and Technologies E-Learning - CompSysTech '03 2003

DOI: 10.1145/973620.973693

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Machine learning and expert utility assessment

Yuri Pavlov

K. Ljakova

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2013

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Elements of Stochastic Programming

2013

Decision Control, Management, and Support in Adaptive and Complex Systems

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In the second part of the chapter is discussed the “potential functions method,” a very fruitful area of the stochastic programming. Even though it is called method, this field is actually a mathematical theory whose practical results are a large number of stochastic recurrent procedures for pattern recognition, approximating algorithms for functions in noisy conditions, development of unified mathematical approach for machine learning on the basis of human preference, proof of the perceptron theory, etc. The stochastic algorithms based on the “potential functions method” have stable convergence and flexibility, and these properties permit fruitful application in utility and value function evaluations and polynomial approximations. The last part of the chapter gives an example of pattern recognition of two sets of positive and negative answers as machine learning procedure.

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Elements of Stochastic Programming

2013

Decision Control, Management, and Support in Adaptive and Complex Systems

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Stochastic Utility Evaluation

2013

Decision Control, Management, and Support in Adaptive and Complex Systems

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A mathematically grounded method is offered in the chapter that could be used at the stage of expressing the preferences and constructing the value and utility functions. In this case, it is necessary to take account of some important characteristics of the DM-Computer dialogue such as the conceptual, qualitative nature of the subject’s thinking and the probable and subjective indefiniteness of expressing the expert’s preferences. The utility functions are constructed by the means of stochastic recurrent procedures as a recognition of a set by learning the computer in the same preferences as these of the expert.

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Machine learning and expert utility assessment

Cited by 2 publications

References 6 publications

Elements of Stochastic Programming

Elements of Stochastic Programming

Stochastic Utility Evaluation

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