data envelopment analysis (dea) is a non-parametric method for assessing relative efficiency of decision-making units (dmus). every single decision-maker with the use of inputs produces outputs. these decision-making units will be defined by the production possibility set. resource allocation to dmus is one of the concerns of managers since managers can employ the results of this process to all...

context-dependent modeling is a well-known approach to increase modeling accuracy in continuous speech recognition. the most common way to implement this approach is via triphone modeling. nevertheless, the large number of such models results in several problems in model training, whilst the robust training of such models is often hardly obtained. one approach to solve this problem is via param...

system designs, optimizing resource allocation to organization units, is still being considered as a complicated problem especially when there are multiple inputs and outputs related to a unit. the algorithm presented here will divide the frontiers obtained with dea. in this way, we investigate a new approach for resource allocation.

System designs, optimizing resource allocation to organization units, is still being considered as a complicated problem especially when there are multiple inputs and outputs related to a unit. The algorithm presented here will divide the frontiers obtained with DEA. In this way, we investigate a new approach for resource allocation.

  Data envelopment analysis (DEA) is a non-parametric method for assessing relative efficiency of decision-making units (DMUs). Every single decision-maker with the use of inputs produces outputs. These decision-making units will be defined by the production possibility set. Resource allocation to DMUs is one of the concerns of managers since managers can employ the results of this process to a...

Context-dependent modeling is a widely used technique for better phone modeling in continuous speech recognition. While different types of context-dependent models have been used, triphones have been known as the most effective ones. In this paper, a Maximum a Posteriori (MAP) estimation approach has been used to estimate the parameters of the untied triphone model set used in data-driven clust...