Data compression techniques had been used for applying the models on large data, that is, for large scale classification, dictionary learning while for large scale regression pre-clustering approach had been applied.
However, this has not yet been quantified for large-scale classification of many cover types with subtle differences in complex, noisy hyperspectral patterns.
In their study on multistage adaptive testing for a large-scale classification test (design heuristic assembly, and comparison with other testing modes), Zheng et al. (2012) designed an MSCAT for a large-scale classification test and performed the automated test assembly using a heuristic method.
Both our model itself and the solving algorithm can guarantee that it can deal with large-scale classification problems with a huge number of instances as well as features.
This high speed makes the proposed method well applicable to perform large-scale classification of microorganisms.
Simultaneously, they can also be carried out with other libraries, such as LIBLINER [ 29], which is alternative for SVM classification, especially appropriate for large-scale classification problems.
These include the following: large-scale classification, taxonomic placement of newly-sequenced genomes, and high resolution of CVTree at the rank species and below.
Large-scale classification, or as Cavalier-Smith puts it [56], mega-classification, of prokaryotes, deals with higher taxonomic ranks such as phylum, class, and order (at present, ranks higher than order are not covered by the International Bacterial Code [57]).
Linear models are a feasible tool for large-scale classification and regression tasks such as linear support vector machines (linear SVM) and logistic regression which provide comprehensible models for these tasks.
In our method, to represent drug pairs with their target information, the estimation of drug-drug relationships was restated as a large-scale classification problem that distinguished drug pairs with a common target.
