Author: Thomas Zeugmann
Source: Stochastic Algorithms: Foundations and Applications, International Symposium SAGA 2001, Berlin, Germany, December 13-14, 2001, Proceedings,'' (Kathleen Steinhöfel, Ed.), Lecture Notes in Computer Science 2264, pp. 155 - 171, Springer 2001.
Abstract. Recently, we have a developed a learning model, called stochastic finite learning, that makes a connection between concepts from PAC learning and inductive inference learning models.
The motivation for this work is as follows. Within Gold's (1967) model of learning in the limit many important learning problems can be formalized and it can be shown that they are algorithmically solvable in principle. However, since a limit learner is only supposed to converge, one never knows at any particular learning stage whether or not it has already been successful. Such an uncertainty may be not acceptable in many applications.
The present paper surveys the new approach to overcome this uncertainty that potentially has a wide range of applicability.
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