Abstract. We solve the problem of discriminating with minimum error probability two given Pauli channels. We show that, differently from the case of discrimination between unitary transformations, the use of entanglement with an ancillary system can strictly improve the discrimination, and any maximally entangled state allows to achieve the optimal discrimination. We also provide a simple neces...

— The minimum mean-squared-error (MMSE) linear multiuser detector [1–2] is popular because of its good performance and amenability to adaptive implementation. However , there are circumstances in which the linear detector that minimizes bit-error rate (BER) can significantly outperform the MMSE detector. We propose a low-complexity adaptive algorithm for approximating the minimum-BER linear mul...

Minimum distances, distance distributions, and error exponents on a binary-symmetric channel (BSC) are given for typical codes from Shannon’s random code ensemble and for typical codes from a random linear code ensemble. A typical random code of length and rate is shown to have minimum distance (2 ), where ( ) is the Gilbert–Varshamov (GV) relative distance at rate , whereas a typical linear co...

The design of optimal feature sets for visual classification problems is still one of the most challenging topics in the area of computer vision. In this work, we propose a new algorithm that computes optimal features, in the minimum Bayes error sense, for visual recognition tasks. The algorithm now proposed combines the fast convergence rate of feature selection (FS) procedures with the abilit...

This paper develops and explores applications of a linear shaping transformation that minimizes the mean squared error (MSE) between the original and shaped data, i.e., that results in an output vector with the desired covariance that is as close as possible to the input, in an MSE sense. Three applications of minimum MSE shaping are considered, specifically matched filter detection, multiuser ...

We propose a Minimum Verification Error (MVE) training scenario to design and adapt an HMM-based speaker verification system. By using the discriminative training paradigm, we show that customer and background models can be jointly estimated so that the expected number of verification errors (false accept and false reject) on the training corpus are minimized. An experimental evaluation of a fi...

Least-squares approximation can be used in image-based colorimetry once the set of sampling functions is selected. We discuss the accuracy obtained in earlier research, and we compare it with a better calculation method.

Distinguishing different quantum states is a fundamental task having practical applications in information processing. Despite the effort devoted so far, however, strategies for optimal discrimination are known only for specific examples. In this paper we consider the problem of minimum-error quantum state discrimination where one attempts to minimize the average error. We show the general stru...

Minimum error rate training is a crucial component to many state-of-the-art NLP applications, such as machine translation and speech recognition. However, common evaluation functions such as BLEU or word error rate are generally highly non-convex and thus prone to search errors. In this paper, we present LP-MERT, an exact search algorithm for minimum error rate training that reaches the global ...

Perceptual linearization has been advocated for medical image presentation, both for the faithful reproduction of images, and for standardizing the appearance across different display devices. At this time, few studies have attempted to evaluate the effectiveness of a post-processing perceptual linearization step. We have recently analyzed the methods for computing perceptual linearization rema...