Decomposing parameters of mixture Gaussian model using genetic and maximum likelihood algorithms on dental images

Non-parametric location estimation in rough wireless environments for wireless sensor network

“…. ., G , respectively [25,26]. The pdf of the observationd j (k) at time k can be represented in the finite mixture form:…”

Sample size for maximum-likelihood estimates of Gaussian model depending on dimensionality of pattern space

“…Gaussian modeling is the subject of constant research by statisticians [14], [15], [16]. It is typically used in tasks of pattern recognition [17], [18], [19], [20], [21], [22],computer vision [23], [24], [25], speech and language recognition [26], [27], [28], machine learning [29], [30]; however, it also finds use in very different areas such as medical or technical diagnostics [31], [32], [33], [34], [35], [36].…”

Fault diagnosis for a solar assisted heat pump system under incomplete data and expert knowledge

“…Pernkopf et al [22] present a genetic based EM algorithm for learning Gaussian mixture models form multivariate data and the algorithm is less sensitive to the initialization compared to the standard EM. Majdi-Nasab et al [23] propose new approaches based on genetic algorithms, simulated annealing and EM for parameter learning of the mixture Gaussian model. Huda et al [24] present a hybrid algorithm for estimation of the hidden Markov model in automatic speech recognition using a constraint-based evolutionary algorithm and EM and the presented algorithm overcome the problem of EM converging to a local optimum.…”