Last Success Problem: Decision Rule and Application

Decision Rule and Application

An Iterative Decision Rule to minimize cost of Acceptance Sampling Plan in Machine Replacement Problem

In this paper, we presented an optimal iterative decision rule for minimizing total cost in designing a sampling plan for machine replacement problem using the approach of dynamic programming and Bayesian inferences. Cost of replacing the machine and cost of defectives produced by machine has been considered in model. Concept of control threshold policy has been applied for decision making. If ...

The "Last" Decision Problem for Rational Trace Languages

It is established here that it is decidable whether a rational set of a free partially commutative monoid (i.e. trace monoid) is recognizable or not if and only if the commutation relation is transitive (i.e. if the trace monoid is isomorphic to a free product of free commutative monoids). The bulk of the paper consists in a characterization of recognizable sets of free products via generalized...

Solving Multiple-Instance and Multiple-Part Learning Problems with Decision Trees and Rule Sets. Application to the Mutagenesis Problem

In recent work, Dietterich et al. (1997) have presented the problem of supervised multiple-instance learning and how to solve it by building axis-parallel rectangles. This problem is encountered in contexts where an object may have di erent possible alternative con gurations, each of which is described by a vector. This paper introduces the multiple-part problem, which is related to the multipl...

An application of the data mining methods with decision rule

Rankings for output of Chinese main agricultural commodity in the world for 1978, 1980, 1990, 2000, 2006, 2007 and 2008 have been released in United Nations FAO Database. Unfortunately, where the ranking of output of Chinese cotton lint in the world for 2008 was missed. This paper uses sequential data mining methods with decision rules filling this gap. This new data mining method will be help ...