In this paper we propose an algorithm we call \NeuralBAG" that estimates the set of weights and number of hidden units each network in a bagged ensemble should have so that the generalization performance of the ensemble is optimized. Experiments performed on noisy synthetic data demonstrate the potential of the algorithm. On average, ensembles trained using NeuralBAG out-perform bagged networks...

The latest HEVC video coding standard [1] has improved the coding performance by applying a number of innovative tools compared to its predecessor H.264/AVC [2][3] including a wider range of variable block size motion estimation (ME), motion compensation (MC), prediction, and transformation units. The use of multiple reference frames (MRFs) with variable block sizes typically provides better co...

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classification problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classifica...

Sample complexity results from computational learning theory, when applied to neural network learning for pattern classiication problems, suggest that for good generalization performance the number of training examples should grow at least linearly with the number of adjustable parameters in the network. Results in this paper show that if a large neural network is used for a pattern classiicati...

The Current Population Survey (CPS) adjusts the sampling weights for nonresponse to match population controls based on cells which combine similar primary sampling units (PSU) based on size and urbanicity. The adjustment method assumes that the nonresponse is random within the adjustment cells. This adjustment increases weights for responding units in PSUs with higher nonresponse. The present s...

The performance of an organization is a function of its efficiency and effectiveness. Efficiency is the degree to which inputs are used to produce outputs. Effectiveness is the degree to which organizational goals are met. Efficiency provides a starting point for our discussion, as it continues to be an active area of research. Efficiency is defined as the ratio of total output to total input. ...

one of the difficulties of data envelopment analysis(dea) is the problem of deciency discriminationamong efficient decision making units(dmus) and hence, yielding large number of dmus as efficientones. the main purpose of this paper is to overcome this inability. one of the methods for rankingefficient dmus is minimizing the coefficient of variation (cv) for inputs-outputs weights. in this pap...

‎In this paper, we present a two-stage model for ranking of decision making units (DMUs) using interval analytic hierarchy process (AHP). Since the efficiency score of unity is assigned to the efficient units, we evaluate the efficiency of each DMU by basic DEA models and calculate the weights of the criteria using proposed model. In the first stage, the proposed model evaluates decision making...

This paper proposes a method for ranking decision making units (DMUs) using some of the multiple criteria decision making / multiple attribute decision making          (MCDM /MADM) techniques, namely, interval analytic hierarchy process (IAHP)          and the technique for order preference by similarity to an ideal solution (TOPSIS).          Since the efficiency score of unity is assigned to ...