Presents a training algorithm for probabilistic neural networks (PNN) using the minimum classification error (MCE) criterion. A comparison is made between the MCE training scheme and the widely used maximum likelihood (ML) learning on a cloud classification problem using satellite imagery data.

In this paper, we present results for the Minimum Classi cation Error (MCE) [1] framework for discriminative training applied to tasks in continuous phoneme recognition. The results obtained using MCE are compared with results for Maximum Likelihood Estimation (MLE). We examine the ability of MCE to attain high recognition performance with a small number of parameters. Phoneme-level and string-...

In our previous work, we have proposed a speaker modeling technique using spectral and pitch features for text-independent speaker identification based on Multi-Space Probability Distribution Gaussian Mixture Models (MSD-GMMs). We have presented a maximum likelihood (ML) estimation procedure for the MSD-GMM parameters and demonstrated its high recognition performance. In this paper, we describe...

We propose a deterministic annealing (DA) algorithm to design classifiers based on continuous observation hidden Markov models. The algorithm belongs to the class of minimum classification error (MCE) techniques that are known to outperform maximum likelihood (ML) design. Most MCE methods smooth the piecewise constant classification error cost to facilitate the use of local descent optimization...

In this study, a new hidden Markov model that integrates generalized dynamic feature parameters into the model structure is developed and evaluated using maximum-likelihood (ML) and minimum-classification-error (MCE) pattern recognition approaches. In addition to the motivation of direct minimization of error rate, the MCE approach automatically eliminates the necessity of artificial constraint...

This paper investigates the use of minimum classification error (MCE) training in conjunction with speaker adaptation for the large vocabulary speech recognition task of lecture transcription. Emphasis is placed on the case of supervised adaptation, though an examination of the unsupervised case is also conducted. This work builds upon our previous work using MCE training to construct speaker i...

Abstract The magnetocaloric effect (MCE) of Ni 0.4 Cu 0.2 Zn Fe 2- x Dy O 4 ( = 0.02, 0.03, and 0.04) nanoferrites is simulated using a phenomenological model. analysis indicates that the MCE strongly influenced by content in both conventional inverse MCE. For MCE, full-width at half-maximum $$\delta {\text{T}}_{{{\text{FWHM}}}}$$ <mml:m...

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...

The primary background for the present study was a project to assist the authorities in Thailand with development of plans for how to deal with the future tsunami risk in both short and long term perspectives, in the wake of the devastating 26 December 2004 Sumatra-Andaman earthquake and tsunami. The study is focussed on defining and analyzing a number of possible future earthquake scenarios (m...