Relevance feedback is an effective and widely accepted method in information retrieval to improve performance. Relevance feedback generally uses an adaptive learning method to estimate the user’s information need. In this research, we propose an alternative two-stage sampling method to obtain an unbiased estimate of the user’s information need. Our estimate shows not only improved retrieval per...

This result is obvious but also useful because it tells us that the sample mean is an unbiased estimate for the exact mean, in the sense that the average of the sample mean, over many repetitions, is the exact mean. An unbiased estimate will become more and more accurate as the number of data points is increased. However, a biased estimate not continue to improve with increasing N once the erro...

Iterative thresholding algorithms are well-suited for high-dimensional problems in sparse recovery and compressive sensing. The performance of this class of algorithms depends heavily on the tuning of certain threshold parameters. In particular, both the final reconstruction error and the convergence rate of the algorithm crucially rely on how the threshold parameter is set at each step of the ...

In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising. In particular, we derive the closed-form of the optimal blockwise shrinkage for NLM that minimizes the Stein’s unbiased risk estimator (SURE). We also propose a constant complexity algorithm allowing fast blockwise shrinkage. Simulation results show that the proposed blockwise shrinkage method im...

We attempt to recover a function of unknown smoothness from noisy, sampled data. We introduce a procedure, SureShrink, which suppresses noise by thresholding the empirical wavelet coe cients. The thresholding is adaptive: a threshold level is assigned to each dyadic resolution level by the principle of minimizing the Stein Unbiased Estimate of Risk (Sure) for threshold estimates. The computatio...

budget plays an important role in public sector management and administration .one of its components is revenues forecasting and without the necessary accuracy in predicting revenues, government deficit will occur and people are incapable of facing with losses of reducing of important and essential services like education and health care. in this study, the rational expectations hypothesis is a...

BACKGROUND Epidemiologic studies using electronic healthcare data often define the presence or absence of binary clinical outcomes by using algorithms with imperfect specificity, sensitivity, and positive predictive value. This results in misclassification and bias in study results. METHODS We describe and evaluate a new method called probabilistic outcome definition (POD) that uses logistic ...