Sentence examples for discovering classification from inspiring English sources

Because EvoGrader utilizes supervised machine learning, which in turn relies on discovering classification rules in a corpus of human-classified data, very large pre-scored data sets (text corpora) are required for both system training (i.e., learning from existing data) and system testing (i.e., examining the strength of classification algorithms).

In order to discover classification rules, we propose a hybrid decision tree/genetic algorithm method.

The primary goal of supervised machine learning is to discover classification rules given a corpus of classified data and then to apply those rules to score new unlabeled data.

Cluster analysis is a term used to describe a family of statistical procedures specifically designed to discover classifications within complex data sets.

New integrative strategies for early diagnosis could greatly enhance the timeliness of therapy planning, and interest in discovering reliable classification and prediction methods for pain patients is accordingly considerable (Baron, 2006; Finnerup and Jensen, 2006; Meyer-Rosberg et al., 2001).

Because the results of different phylogenetic analyses vary among researchers, and will continue to change as new specimens and taxa are discovered, the classification can be expected to change accordingly.

However, in a field where new syndromes are being discovered and classifications regularly updated, these rates should only be accepted as provisional.

Ant colony optimization (ACO) algorithms have been successfully applied in data classification, which aim at discovering a list of classification rules.

Two typical problems that researches want to solve using microarray data are: (1) discovering informative genes for classification based on different cell-types or diseases [ 1]; (2) clustering and arranging genes according to their similarity in expression patterns [ 2].

Synack, named for one of the steps in the transmission control protocol, has developed a taxonomy for security breaches and pays out its bounties based on where a security threat that's discovered falls within its classifications — basically discovering easy vulnerabilities pays less than rooting out more advanced threats.

Recently, through an unsupervised transcriptome analysis, we discovered a molecular classification of ccRCC with four robust subgroups (ccrcc1 to 4) related to previous molecular classifications [ 2, 3] and associated with outcome on sunitinib [ 4].