CPC uses six features of putative ORFs to define transcripts as protein-coding whose conceptual translations are relatively long and/or that are sequence-similar to known proteins.
Nevertheless, the final classifier used four features because of our strategy of simplifying the model by also minimizing the number of support vectors (see "Materials and Methods").
One feature for robot capabilities and components (Robot characteristics), to test Scalability we use two features (maximum number of robots and scalability).
To compare the complexity of environments we use two features (Size and limits of the world, Number and nature of food).
We used three feature selection methods of increasing complexity.
The rule-based algorithm used three major features of the GPS data (i.e., time, speed, and spatial location) to classify GPS data to four major time activity categories: indoor, outdoor static, outdoor walking, and in-vehicle travel.
Nguyen and Moschitti [40] presented a method for incorporating global features in NER using re-ranking techniques that used two kinds of features, i.e., flat and structured features and a combination of CRF and SVM.
Lee et al., [11] used two models of features for objectionable video classification.
In the following optimization study, we will use two key features of this function: a) it is monotonously increasing, and b) concave in P u (or p c u, c ∈ C, respectively).
Computer simulations are used to investigate two features of proteins: ligand binding and ligand entry/exit.
