An attribute grammar is pure (left-to-right) multi-pass if a bounded number of left-to-right passes over the derivation tree suffice to compute all its attributes. There is no requirement, as for the usual multi-pass attribute grammars, that all occurrences of the same attribute are computed in the same pass, R is shown that the problem of determining whether an arbitrary attribute grammar is p...

Our basic idea is to employ association rule for the purpose of data quality measurement. Strong rule generation is an important area of data mining. We purpose a Genetic Algorithm to generate high quality Association Rules with four metrics they are confidence, completeness, interestingness and comprehensibility. These metrics are combined as an objective fitness function. Fitness function eva...

Multi-attribute resource allocation problems involves the allocation of resources on the basis of several attributes, therefore, the definition of a fairness method for this kind of auctions should be formulated from a multidimensional perspective. Under such point of view, fairness should take into account all the attributes involved in the allocation problem, since focusing in just a single a...

The idea of evolutionary game theory is to relate the payoff of a game to reproductive success (= fitness). An underlying assumption in most models is that fitness is a linear function of the payoff. For stochastic evolutionary dynamics in finite populations, this leads to analytical results in the limit of weak selection, where the game has a small effect on overall fitness. But this linear fu...

this paper describes a methodology for the integration of well logs and a series of grid-based attributes extracted from interpreted seismic data for prediction of porosity distributions. the studied area is located in the southwest iran. before finding a relationship between the target logs and predicted logs from 3d seismic data, we have interpreted the 3d seismic data in the studied area. al...

We present a new type of multi-class learning algorithm called a linear-max algorithm. Linearmax algorithms learn with a special type of attribute called a sub-expert. A sub-expert is a vector attribute that has a value for each output class. The goal of the multi-class algorithm is to learn a linear function combining the sub-experts and to use this linear function to make correct class predic...