Alkhalifah and Fomel (2009, 2011) have recently extended this algorithm to transversally isotropic media.
Furthermore we extend this algorithm to identify and revise other types of fuzzy measures.
Second, we extend this algorithm to a discrete-time form by using the fourth-order Runge Kutta method.
A forthcoming improvement will be to extend this algorithm to deal with extremely large number of classes (e.g. up to thousands of classes).
Section 3 briefly introduces random forests of oblique decision trees and then extend this algorithm to multi-classes classification of very-high-dimensional datasets.
We also extend this algorithm to carry out parametric minimization for a strong map sequence of submodular functions in the same asymptotic running time as a single SFM.
In the presented work, we extend this algorithm to be used for high-performance virtual database screening and investigate, whether applying this geometrically more accurate 3D alignment algorithm improves virtual screening results over conventional incremental n-point distance matching approaches.
More precisely, we developed an algorithm based on graph decomposition and dynamic programming, and we further extended this algorithm with a backtracking scheme, which achieved additional run-time reductions for the dynamic programming part of up to 48%.
However, it is not straightforward how to extend this algorithm to compute marginal posterior genotype probabilities for pedigrees with loops.
We previously demonstrated that the inference of these so-called coexpressed PPI modules has a high reproducibility across multiple expression datasets in breast cancer (van den Akker et al., 2011), and here we extend this algorithm to combine multiple gene expression datasets on aging.
