Sentence examples for maximization approach from inspiring English sources
The field synergy number maximization approach for heat exchanger optimization design is thus formulated.
Specifically, we study the decision process involved in identifying destination locations after picking up a bicycle at a BSS station, using a random utility maximization approach in the form of a multinomial logit model (MNL).
GMMSE applies K-means to initialize the values of the parameters of the Gaussian mixture model, and adopts the Expectation Maximization approach (EM) to estimate the parameter values of the model.
The comparison with the traditional heat exchanger optimization design approach with the total cost as the objective function shows that the field synergy number maximization approach is more advantageous.
Each r i is then used to estimate the corresponding ε i by following an expectation-conditional maximization approach.
The model is trained offline on different percussive sounds using the expectation maximization approach for learning spectral templates for each sound and is able to run online to detect and classify sounds from audio stream input by a Hidden Markov Model.
The rest of this paper is organized as following: "Related work" section reviews an indispensable comparison for existing community detection algorithms, be it from Betweenness algorithm to modularity maximization approaches to.
Closely related to distance minimization are the similarity maximization approaches.
Plan et al. investigated parametric approaches for maximum likelihood estimation: first-order conditional estimation in NONMEM and R, LAPLACE in NONMEM and SAS, adaptive Gaussian quadrature in SAS, and stochastic approximation expectation maximization in NONMEM and MONOLIX (both stochastic approximation expectation maximization approaches with default and modified settings).
An information-maximization approach to blind separation and blind deconvolution.
An iterative Expectation-Maximization approach allows to compute, for each earthquake i, the background probability as ωi = μ ti /λ ti).
