The classical Fisher linear classification analysis is a well known linear classification procedure. This technique tends to minimize the misclassification error if the data set comes from a multivariate normal distribution. On the other hand, if the data set is contaminated, the misclassification error of this approach tends to increase. Relying on the classification performance of the classic...

In December 1987, the Centers for Disease Control (CDC) proposed to the Health Care Financing Administration revised criteria for evaluating participating laboratories' performance in proficiency-testing programs. If these criteria are accepted, they will become the minimum standard for all regulatory proficiency-testing programs. To evaluate a laboratory's performance in a clinical chemistry p...

This paper studies several discriminative models for spoken language understanding (SLU). While all of them fall into the conditional model framework, different optimization criteria lead to conditional random fields, perceptron, minimum classification error and large margin models. The paper discusses the relationship amongst these models and compares them in terms of accuracy, training speed ...

We provide formulas for the norm of the error when solving non symmetric linear systems with the full orthogonalization method (FOM) and the generalized minimum residual method (GMRES) as well as relations between the error norm and the residual norm. From these formulas we are able to compute estimates of the norm of the error during the iterations. Since stopping criteria based on the norm of...

Resolution of binary mixtures of theophylline (THEO), montelukast (MKST) and loratadine (LORA) with minimum sample pretreatment and without analyte separation has been successfully achieved by multivariate spectrophotometric calibration, together with partial least-squares (PLS-1), principal component regression (PCR) and hybrid linear analysis (HLA). Data of analysis were obtained from UV–Vis ...

This article considers the analysis of experiment of repeated measures design that is used frequently in medical research. We propose new approach could be used to guide the selection of the covariance structure. We used simulation study to compare six model selection criteria in terms of their ability to identify the right correlated error model with the help of the new approach. The compariso...

precipitation is considered as one of the most important factures in water cycle. prediction of monthly precipitation is important for many purposes such as estimating torrent, drought, run-off, sediment, irrigation programming and also management of drainage basins.in this study we studied and evaluated gene expression programming to predict the precipitation of the kakareza river (in lorestan...

Training neural network acoustic models with sequencediscriminative criteria, such as state-level minimum Bayes risk (sMBR), been shown to produce large improvements in performance over cross-entropy. However, because they entail the processing of lattices, sequence criteria are much more computationally intensive than cross-entropy. We describe a distributed neural network training algorithm, ...

One of the most commonly used discriminative approaches in parameter estimation for Hidden Markov Models is the Minimum Classification Error (MCE) method ([1]). This paper studies possible choices for the classes (i.e. basic speech units) in MCE training and their application for several tasks suitable for speech driven dialog systems in the telephone environment. The considered choices of clas...