GMMSGlobal Marketing Management System (book)
GMMSGranite Mountain Middle School (Prescott, AZ)
GMMSGrizzly Mountain Medical Services (Canada)
GMMSGeosynthetic Membrane Monitoring System (Sandia National Laboratories)
GMMSGovernor Mifflin Middle School (Shillington, PA)
GMMSGood Morning Metro South (Metro South Chamber of Commerce; Brockton, MA)
Copyright 1988-2018, All rights reserved.
References in periodicals archive ?
Table 2 shows that the GMMs exhibited the best forecast quality measures, corresponding to the highest values of DAC and lowest errors (MAE, RMSE, MAPE, RAE and RRSE), compared to SMMs and UMs.
Let M(h) denotes the mean supervector in a GMM which is related to languages and channels.
Gaussian mixture models (GMM) have been introduced for speaker verification [1] and become the dominant modeling approach until recently.
The initial user initializations are interpolated in the corresponding slices to mark the thyroid and nonthyroid regions (i.e., foreground and background) and create corresponding GMMs. The aforementioned processes are then repeated in each individual images to segment all the thyroid in the dataset.
It is worthwhile noting that, after multiple statistical fits of the empirical data to GMMs, we decided to abandon the GMM in favor of a simple Gaussian fit since the latter performs comparably with the former with respect to classification while being much less computationally intensive.
Then the system uses GMM model to filter the top 20 results, extracts the GMM main color feature, and computes the similarity of them.
HMMs differ from GMMs in terms of input temporal evolution.
Similarly, another colour image indexing method through spatiochromatic multi-channel GMM was introduced by Piatek and Smolka [25].
[9.] Jwu-Sheng Hu, Member, IEEE, Chieh-Cheng Cheng And Wei-Han Liu, 2006." robust speaker's location detection in a vehicle environment using GMM models", in IEEE transactions on systems, man, and cybernetics--part b: cybernetics, 36-2.
In this case, in order to obtain a uniform distribution, length of GMMs near the center is supposed to be shorter and therefore sets a lower boundary for parametrical optimization:
Gaussian mixture models (GMMs) were used to estimate the intensity distributions of foreground/background seeds [30].
Even if the orthogonality of the regressors to the error is violated, this log-linear specification is easily extended to the linear GMMs estimation.