In data envelopment analysis (DEA), mul-tiplier and envelopment CCR models eval-uate the decision-making units (DMUs) under optimal conditions. Therefore, the best prices are allocated to the inputs and outputs. Thus, if a given DMU was not efficient under optimal conditions, it would not be considered efficient by any other models. In the current study, using common weights in DEA, a number of...

Parallel perceptrons (PPs), a novel approach to committee machine training requiring minimal communication between outputs and hidden units, allows the construction of efficient and stable nonlinear classifiers. In this work we shall explore how to improve their performance allowing their output weights to have real values, computed by applying Fisher’s linear discriminant analysis to the commi...

We propose a semiparametrically efficient estimator for α-risk-minimizing portfolio weights. Based on the work of Bassett et al. 2004 , an α-risk-minimizing portfolio optimization is formulated as a linear quantile regression problem. The quantile regression method uses a pseudolikelihood based on an asymmetric Laplace reference density, and asymptotic properties such as consistency and asympto...

Aderivative-free, delayedweight update law is developed formodel reference adaptive control of continuous-time uncertain systems, without assuming the existence of constant ideal weights. Using a Lyapunov–Krasovskii functional it is proven that the error dynamics are uniformly ultimately bounded, without the need for modification terms in the adaptive law. Estimates for the ultimate bound and t...

We describe a method for incrementally constructing a hierarchical generative model of an ensemble of binary data vectors. The model is composed of stochastic, binary, logistic units. Hidden units are added to the model one at a time with the goal of minimizing the information required to describe the data vectors using the model. In addition to the top-down generative weights that define the m...

The calculation of second derivatives is required by recent training and analysis techniques of connectionist networks, such as the elimination of superfluous weights, and the estimation of confidence intervals both for weights and network outputs. We review and develop exact and approximate algorithms for calculating second derivatives. For networks with |w| weights, simply writing the full ma...

Consider the Hopfield network [3], a “neural network,” with symmetrical connections between binary neural “units.” Hopfield showed how such a network could learn: patterns were “imposed” on the network, and connections modified by local Hebbian learning. Remarkably, the network could “fill in” patterns from fragments, providing a form of “content-addressable memory.” Hopfield showed, too, that ...

Parallel processors offer a very attractive mechanism for the implementation of large neural networks. Problems in the usage of parallel processing in neural computing involve the difficulty of handling the large amount of global communication between processing units and the storage of weights for each of the neural processor connections. This paper wi l l discuss how massive parallelism in th...

In this paper, we present a model allowing to determine the weights related to interacting criteria. This is done on the basis of the knowledge of a partial ranking over a reference set of alternatives (prototypes), a partial ranking over the set of criteria, and a partial ranking over the set of interactions between pairs of criteria.