This paper describes a two-step Viterbi decoding based on reinforcement learning and information theory with telephone speech. The idea is to strength or weaken HMM’s by using Bayesbased confidence measure (BBCM) and distances between models. If HMM’s in the N-best list show a low BBCM, the second Viterbi decoding will prioritize the search on neighboring models according to their distances to ...

We develop a new semantics for defeasible infer­ ence based on extended probability measures al­ lowed to take infinitesimal values, on the inter­ pretation of defaults as generalized conditional probability constraints and on a preferred-model implementation of entropy-maximization.

The expectation-maximization (EM) algorithm aims to nd the maximum of a log-likelihood function, by alternating between conditional expectation (E) step and maximization (M) step. This survey rst introduces the general structure of the EM algorithm and the convergence guarantee. Then Gaussian Mixture Model (GMM) are employed to demonstrate how EM algorithm could be applied under Maximum-Likelih...


 This paper discuss about the use face patteren recognition which is now days become popular especialy on smartphone lock screen system. The method used in this research are Expectation – Maximization (EM) Algorithm. EM Algorithm an iterative optimization for estimation of Maximum Likelihood (ML) incomplete data problems. there 2 stages, namely stage E (E-step) and M (M-step). These two s...

The Expectation{Maximization (EM) algorithm is an iterative procedure for maximum likelihood parameter estimation from data sets with missing or hidden variables [2]. It has been applied to system identi cation in linear stochastic state-space models, where the state variables are hidden from the observer and both the state and the parameters of the model have to be estimated simultaneously [9]...