Recently, feature selection methods using information theory have been devised for feature-to-class relevance and feature-to-feature correlations, including a probabilistic interpretation based upon the conditional likelihood maximisation in order to unify information theoretic feature selection [ 36].
Several techniques [ 30– 32] have been devised for making feature selection more stable for biomarker reproducibility.
In recent years, many feature selection methods have been devised for finding such discriminative genes, and more recently information theoretic filters have also been introduced for capturing feature-to-class relevance and feature-to-feature correlations in microarray-based classification.
Detailed country programmes have been devised for Iraq, Sri Lanka, Colombia, Albania and Ukraine.
Three major goals were devised for MFS: minimal feature size, maximal internal MCC value, and maximal external MCC value.
Using both biological observation and computer-vision consideration, a new framework is devised for pore-scale facial-feature extraction and matching.
A novel quadrilateral feature is devised for this task, which models the shapes of commonly present façade elements, such as windows.
Secondly, a feature granularity concurrency control approach is devised for replicated co-design systems to ensure the consistency of replicated models after concurrent manipulations.
An extraction method based on the self-organizing map (SOM) feature and the WANN model was devised for modeling the rainfall-runoff.
Interactive and personal wayfinding themes can also be devised for guidance by text, audio, video, etc. in terms of location information, such as the features of interest of a particular place.
