Both Experiments 3a and 3b resulted in large categorization shifts.
Functional categorization assigned a large number of unigenes to involvement in intracellular cell components, membranes, organelles, metabolic processes, translation, transport, oxidation and reduction processes, enzyme activity, binding, structural molecule activity and catalytic activity.
Li and Perona [33] proposed two variants of LDA that generate intermediate topic representations for natural scene categories, reporting good categorization performance on a large set of complex scenes.
It should be noted that even if a more stringent categorization criterion was used in the prediction phase of the analysis, such as a label being selected 70%% rather than 50%% of the time (see Bundgaard-Nielsen, et. al. 2011), this would not have made a large difference to the number of categorizations in the VO condition, nor in any condition for the Cantonese group (see the Appendix).
The most significant categorizations that emerged included a large contingent of genes related to DNA-binding and transcription, the most abundant being zinc-finger proteins.
Structural analysis of a large number of intrinsic disorder-based protein complexes resulted in another categorization of IDRs based on their binding plasticity.
If early categorization is difficult because processing complexity is high (for a large N and long words a large number of similar memory entries or features must be processed), the P1 tends to be large.
This surface clearly demonstrated that fitting the data requires a large shift away from the balanced control value in the post-categorization bias, but not in input gain.
The categorizations are based on different criteria in different studies and thus present a large degree of uncertainties [21].
These new features can describe a large amount of data in a lower dimensional space, which makes the PCA a useful tool for data categorization.
