One way to construct such a decision tree, is by using a divide-and-conquer approach to reach the desired output by performing a sequence of tests on each attribute node and splitting the node on each of its possible value.
From Ostrowski's scheme adding one step of Newton with 'frozen' derivative and by using a divided difference operator we construct an iterative scheme of order six for solving nonlinear systems.
In addition to these standard algorithms we also examine SATé, which attempts to improve MSA estimates by using a divide-and-conquer approach to MSA based on MAFFT and the phylogenetic tree inference program RAxML to update the guide tree.
By using a particular divided difference of first order we can transform many iterative methods into derivative-free iterative schemes, holding the order of convergence of the departure original method.
Previous work [ 22] had confirmed that iterative assembly, which relies on a sequence reference map, constructs unbiased optical maps that are essentially equivalent to those crafted by a de novo method using a "divide and conquer" approach [ 22, 23, 26, 46].
K-GIDDI begins by constructing an initial DDI network from cross-species PPI networks, which is then expanded by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information (Ashburner et al., 2000).
K-GIDDI infers an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by GO information.
K-GIDDI constructs an initial DDI network from cross-species PPI networks, and then expands the DDI network by inferring additional DDIs using a divide-and-conquer biclustering algorithm guided by Gene Ontology (GO) information, which identifies partial-complete bipartite sub-networks in the DDI network and makes them complete bipartite sub-networks by adding edges.
The proposed method is capable of handling domains with arbitrary geometry by using a circular boundary to divide these into near field and far field.
