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Because the analysis requires an ultrametric tree, one was constructed using a penalized likelihood method also implemented in ape.
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With some necessary modifications, some global methods also implemented in the very large scale integration circuit [14, 15] at the expense of considerable high consumption of logic source, memory and bandwidth.
The methods also implement leave-one-out (LOO) cross-validation for estimating the predictive ability in either case.
Additionally, a comparison between SEC method and existing methods is also implemented, which shows that the SEC method is also efficient in tooling design.
The regular SS method and the autoregressive MED (ARMED) method are also implemented for comparison.
The ElectroShape method was also implemented as a Python module since there is no reference implementation provided by the authors.
The method is also implemented using some numerical examples and the output results are discussed.
MATLAB built-in package bvp4c, based on collocation method, is also implemented for generating numerical results.
This method is also implemented in well-known libraries such as IMSL Numerical Library and Scilab for solving quadratic programming problem.
A novel Euclidian distance and joint entropy-PCA method are also implemented for best feature selection from the fused vector.
This method is also implemented on noisy images at various levels of noise density, and good results are obtained.
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