In contrast, certain neural networks are trained through unsupervised learning, in which a network is presented with a collection of input data and given the goal of discovering patterns without being told what specifically to look for.
Since the goal of a supertree analysis is to synthesize the phylogenetic data from a collection of input trees, it makes sense that an effective supertree method should directly seek the supertree that is most similar to the input trees.
[Comparability] The trees G and S are comparable if there exists a leaf-mapping ℒ G, S. A set of gene trees 𝒢 and S are comparable if each gene tree in 𝒢 is comparable with S. Throughout the remainder of this article, 𝒢 denotes a collection of input gene trees, S a comparable species tree, and G denotes an arbitrary gene tree in 𝒢.
To this end, we show that the associated decision problem, to be referred to as the CIS, of determining whether a subset (of a given collection of inputs) with a prescribed cardinality exists that ensures structural controllability, is NP-complete.
Simultaneous stabilization for a collection of multi-input nonlinear systems with uncertain parameters is dealt with in this paper.
In addition, neutral exclusive regulation also leads to a collection of single input modules -the fourth basic motif.
The inference of a topic model with LDA from a collection of N input documents results in T potential functional modules.
The latter case is known as the supertree problem (Gordon, 1986), whereby the aim is to build a tree (the supertree) summarizing the topological information induced by a collection of source (input) trees as well as possible.
In a regression problem, we are interested in elucidating the relationship between a collection of covariates or inputs x1, …, x p, and a continuous dependent output variable, z.
