We investigate maximum likelihood parameter learning in Conditional Random Fields (CRF) and present an empirical study of pseudo-likelihood (PL) based approximations of the parameter likelihood gradient. We show, as opposed to [1][2], that these parameter learning methods can be improved and evaluate the resulting performance employing different inference techniques. We show that the approximat...

In this paper we present a speech recognition and verification method based on the integration of likelihood and likelihood ratio. Speech recognition and verification is unified in one-phase framework. A modified agglomerative hierarchical clustering algorithm is adopted to train the alternative model used in speech verification. In the process of decoding likelihood ratio is combined with like...

The joint log-likelihood of a normally-distributed vector can be expressed as a linear combination of the log-likelihoods of each marginal variable in that random vector. The coefficients of this linear combination are linked to Fisher information concepts, but also to the kriging weights, when the random vector is a spatial distribution. This allows the exact estimation of the posterior or the...

For combining classifiers at measurement level, the diverse outputs of classifiers should be transformed to uniform measures that represent the confidence of decision, hopefully, the class probability or likelihood. This paper presents our experimental results of classifier combination using confidence evaluation. We test three types of confidences: log-likelihood, exponential and sigmoid. For ...

PURPOSE To verify the accuracy of transrectal ultrasound-guided prostatic biopsy (TRUS Bx), magnetic resonance imaging (MRI) and their combination in evaluating the laterality of prostate cancer and to determine the accuracy of MRI in assessing extra-capsular extension of prostate cancer. MATERIALS AND METHODS We retrospectively reviewed our past 100 consecutive series of radical prostatectom...

The likelihood ratio test for m-sample homogeneity of covariance is notoriously sensitive to the violations of Gaussian assumptions. Its asymptotic behavior under non-Gaussian densities has been the subject of an abundant literature. In a recent paper, Yanagihara et al. (2005) show that the asymptotic distribution of the likelihood ratio test statistic, under arbitrary elliptical densities with...

Gaussian process models are flexible, Bayesian non-parametric approaches to regression. Properties of multivariate Gaussians mean that they can be combined linearly in the manner of additive models and via a link function (like in generalized linear models) to handle non-Gaussian data. However, the link function formalism is restrictive, link functions are always invertible and must convert a p...

the technical efficiency involved in cocoa production in cross river state was estimated using the stochastic frontier production function analysis. the effects of some selected socio- economic characteristics of the farmers on the efficiency indices were also estimated. the study relied upon primary data generated from interviewing cocoa farmers using a set of structured questionnaire. a multi...