The traditional data envelopment analysis (DEA) literatures generally concentrated on the efficiency evaluation of single decision making unit (DMU). However, in many practical problems, the decision makers are required to choose a number of DMUs instead of a single one from the DMUs set. Therefore, it is necessary to study the combinatorial efficiency evaluation problem which can be illustrate...

to investigate the effects of drought stress and different combinations of animal manure with super absorbent polymer on yield and yield components of corn, a split plots design was conducted based on a randomized complete block design with 3 replications in fars agricultural research center in 2007. irrigation treatments were assigned as the main factor in three levels: 70, 105 and 140 mm evap...

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Data envelopment analysis (DEA) is non-parametric linear programming (LP) based technique for estimating the relative efficiency of different decision making units (DMUs) assessing the homogeneous type of multiple-inputs and multiple-outputs. The procedure does not require a priori knowledge of weights, while the main concern of this non-parametric technique is to estimate the optimal weights o...

Interactions between secondary structure elements (SSEs) in the core of proteins are evolutionary conserved and define the overall fold of proteins. They can thus be used to classify protein families. Using a graph representation of SSE interactions and data mining techniques we identify overrepresented graphlets that can be used for protein classification. We find, in total, 627 significant gr...

In original data envelopment analysis (DEA) models, the data for all inputs and outputs are known exactly. When some inputs and outputs are unknown decision variables, such as interval data, ordinal data, and ratio bounded data, the DEA model is called imprecise DEA (IDEA). In this paper, We develop an alternative approach based upon slacksbased measure of efficiency (SBM) for dealing with inte...

The decision tree is a popular method to accomplish tying of the states of a set context dependent phone HMMs for efficient and effective training of the large acoustic models. A likelihood-based impurity function is commonly adopted. It is well known that maximizing likelihood does not result in the maximal separation between the distributions in the leaves of the tree. To improve robustness, ...

Introduction: This study presents a classification tree based alternative to crash frequency analysis for analyzing crashes on mid block segments of multilane arterials. Method: The traditional approach of modeling counts of crashes that occur over a period of time works well for intersection crashes where each intersection itself provides a well defined unit over which to aggregate the crash d...

This paper describes a new framework based on one-pass and decision tree based class-triphone acoustic modeling for Mandarin LVCSR. Compared with the multi-pass decoder, it should be more knowledgeable and efficient as all sources are used at the same time when the decoder could be well organized and optimized. We give a detail about the organization of our one-pass decoder and how to handle th...