The minimum mean-square error (MMSE) and minimum error entropy (MEE) are two important criteria in the estimation related problems. The MMSE can be viewed as a robust MEE criterion in the minimax sense, as its minimization is equivalent to minimizing an upper bound (the maximum value) of the error entropy. This note gives a new and more meaningful interpretation on the robustness of MMSE for pr...

The equalisation topic is well researched and a variety of solutions are available. The MAP sequence detector provides the lowest symbol error rate (SER) attainable, and the MLSE offers a near optimal solution. However, these optimal techniques are not yet practical for high-level modulation schemes, due to their computational complexity. Linear equaliser or linear-combiner DFE are practical sc...

Minimum Error Rate Training (MERT) remains one of the preferred methods for tuning linear parameters in machine translation systems, yet it faces significant issues. First, MERT is an unregularized learner and is therefore prone to overfitting. Second, it is commonly used on a noisy, non-convex loss function that becomes more difficult to optimize as the number of parameters increases. To addre...

We consider the problem of designing a linear transformation 2 IR , of rank p n, which projects the features of a classi er x 2 IR onto y = x 2 IR such as to achieve minimum Bayes error (or probability of misclassi cation). Two avenues will be explored: the rst is to maximize the -average divergence between the class densities and the second is to minimize the union Bhattacharyya bound in the r...

Comparing with traditional learning criteria, such as mean square error (MSE), the minimum error entropy (MEE) criterion is superior in nonlinear and non-Gaussian signal processing and machine learning. The argument of the logarithm in Renyis entropy estimator, called information potential (IP), is a popular MEE cost in information theoretic learning (ITL). The computational complexity of IP is...

Often, the training procedure for statistical machine translation models is based on maximum likelihood or related criteria. A general problem of this approach is that there is only a loose relation to the final translation quality on unseen text. In this paper, we analyze various training criteria which directly optimize translation quality. These training criteria make use of recently propose...

This paper presents a self-creating neural network in which a conservation principle is incorporated with the competitive learning algorithm to harmonize equi-probable and equi-distortion criteria. Each node is associated with a measure of vitality which is updated after each input presentation. The total amount of vitality in the network at any time is 1, hence the name conservation. Competiti...

Measurement and verification are one of the prime stages in the entire course of geometrical products in new generation of geometrical product specifications (GPS) standard. Like other kinds of form tolerances, flatness error is one of the important characteristics affecting the functionality and quality of machined components; sufficient efforts have long been made to determine the flatness er...