This article shows that the Minimum Classification Error (MCE) criterion function commonly used for discriminative design of speech recognition systems is equivalent to a Parzen window based estimate of the theoretical Bayes classification risk. In this analysis, each training token is mapped to the center of a Parzen kernel in the domain of a suitably defined random variable. The kernels are s...

We discuss some inference problems associated with the fractional Ornstein-Uhlenbeck (fO-U) process driven by the fractional Brownian motion (fBm). In particular, we are concerned with the estimation of the drift parameter, assuming that the Hurst parameter H is known and is in [1/2, 1). Under this setting we compute the distributions of the maximum likelihood estimator (MLE) and the minimum co...

The concept of multifunctional use of marine offshore areas receives increased significance in the light of sustainable development in heavily used marine areas. Here we defined potential areas in the German EEZ for the co-utilization of offshore wind farms and offshore aquaculture applying integrated multi-trophic aquaculture (IMTA) approaches. We combined a geographic information system (GIS)...

In this paper, we extend the Maximum Likelihood (ML) training algorithm to the Minimum Classiica-tion Error (MCE) training algorithm for discriminatively estimating the state-dependent polynomial coeecients in the stochastic trajectory model or the trended HMM originally proposed in 2]. The main motivation of this extension is the new model space for smoothness-constrained, state-bound speech t...

Both microwave absorption and magnetocaloric effect (MCE) are two essential performances of magnetic materials. We observe that LaFe(11.6)Si(1.4)C(0.2)H(1.7) intermetallic compound exhibits the advantages of both giant microwave absorption exceeding -42 dB and magnetic entropy change of -20 Jkg(-1)K(-1). The excellent electromagnetic wave absorption results from the large magnetic loss and diel...

The evaluation of land quality plays an important role in the study of land resources. Some software of GIS was applied to evaluate land quality in recent years. There is some limitation in Appling GIS to evaluate land quality directly. GIS combined with MCE (Multi-Criteria Evaluation) can eliminate limitation existing in using GIS singly. In this thesis key technology of evaluating land qualit...

A novel method to improve the generalization performance of the Minimum Classification Error (MCE) / Generalized Probabilistic Descent (GPD) learning is proposed. The MCE/GPD learning proposed by Juang and Katagiri in 1992 results in better recognition performance than the maximum-likelihood (ML) based learning in various areas of pattern recognition. Despite its superiority in recognition perf...

Malignant cerebral edema (MCE) is often associated with severe physical disability and a high mortality rate. The current prediction of MCE focused on infarct volume, tools are relatively lacking. prominent veins sign (PVS-SWI) considered marker severely impaired tissue perfusion. This study aimed to determine whether PVS-SWI early-onset MCE. Patients acute ischemic stroke (AIS) due large arter...

In this paper we present a new approach for estimating the interpolation parameters of language models (LM) which are used as classifiers. With the classical maximum likelihood (ML) estimation theoretically one needs to have a huge amount of data and the fundamental density assumption has to be correct. Usually one of these conditions is violated, so different optimization techniques like maxim...

In this paper, a concatenated "super" string model based minimum classification error (MCE) model adaptation approach is described. We show that the error rate minimization in the proposed approach can be formulated into maximizing a special ratio of two positive functions. The proposed string model is used to derive the growth transform based error rate minimization for MCE linear regression (...