This paper analyses three algorithms recently studied in the Computational Learning Theory community: the Gradient Descent (GD) Algorithm, the Exponentiated Gradient Algorithm with Positive and Negative weights (EG algorithm) and the Exponentiated Gradient Algorithm with Unnormalised Positive and Negative weights (EGU algorithm). The analysis is of the form used in the signal processing communi...

Previously reported research efforts demonstrated that a genetic algorithm can evolve coefficients describing transforms that outperform standard wavelets, by reducing the mean squared error (MSE) apparent in reconstructed signals under conditions subject to quantization. This paper describes new results that substantially improve the state-of-the-art in evolved transform performance. Matched f...

readouts (R=4 for a 128x128 grid) and 256 samples per readout for the top row and 32 readouts and 512 samples (R=8 for a 256x256 grid) for the bottom row. A reference gradient echo scan is shown on the left. Mean squared error (units of 10) compared to the gradient echo scan is listed above the image. O-space with prephasing shows the lowest MSE, though strong asymmetric resolution is not obser...

Judgemental forecasting of exchange rates is critical for ®nancial decision-making. Detailed investigations of the potential e€ects of time-series characteristics on judgemental currency forecasts demand the use of simulated series where the form of the signal and probability distribution of noise are known. The accuracy measures Mean Absolute Error (MAE) and Mean Squared Error (MSE) are freque...

Let observations y1, · · · , yn be generated from a first-order autoregressive (AR) model with positive errors. In both the stationary and unit root cases, we derive moment bounds and limiting distributions of an extreme value estimator, ρ̂n, of the AR coefficient. These results enable us to provide asymptotic expressions for the mean squared error (MSE) of ρ̂n and the mean squared prediction err...

We address the problem of reconstructing a random signal from samples of its filtered version using a given interpolation kernel. In order to reduce the mean squared error (MSE) when using a non-optimal interpolation kernel we propose using a scheme with reconstruction rate that is greater than the sampling rate. A digital correction system that processes the samples prior to their multiplicati...

In the present article, motivated by Jeelani et al. (2013), we have made an attempt to develop an improved ratio type estimator of population mean using predictive method of estimation by using linear combination of coefficient of skewness and the quartile deviation of auxiliary variable. The mathematical expressions for the bias and mean squared error (MSE) of the proposed estimator up to the ...

We study the problem of sampling a bandlimited graph signal in the presence of noise, where the objective is to select a node subset of prescribed cardinality that minimizes the signal reconstruction mean squared error (MSE). To that end, we formulate the task at hand as the minimization of MSE subject to binary constraints, and approximate the resulting NP-hard problem via semidefinite program...