"graphical structures" is a valid phrase in written English.
You could use this phrase to refer to any diagram or graph that is used to visualize data or relationships. For example, you could say: "This report includes several graphical structures that show the correlation between population and unemployment."
Directed topic models are generally represented with graphical structures comprising a set of random variables.
We also consider real-world datasets from several domains, aiming to show that multiple graphical structures underlying data can be identified and are able to provide additional problem insight.
Because chemical compounds are often represented by the graph of their covalent bonds, machine learning methods in this domain must be capable of processing graphical structures with variable size.
In this paper, we further investigate the family of asymmetric hidden Markov models (HMM-As), which generalize the emission distributions to arbitrary Bayesian-network distributions, allowing for state-specific graphical structures in the feature space.
The software turns the song "Sound" by Telefon Tel Aviv into graphical structures, so the sounds become visual notations fluctuating along the bars of a bendy stave, in a nod to the bars seen in music notation.
We further identified critical features within and between graphical structures.
Structural learning can be achieved by choosing the graphical structure with maximal likelihood, or its penalised versions, such as AIC and BIC.
Stochastic inference in graphical models may lead to two different types of learning: structural learning, which leads to the estimation of the graphical structure G that best describes the data and quantitative learning, that aims at estimating the parameters of a graphical model, for a given graph.
Most of the structure databases on the Web already have graphical structure input tools.
Any variable (node) in the graphical structure may have any number of parents, including no parents.
Bayesian network is used to represent and predict an uncertain domain in a graphical structure [49].
