Sentence examples for more classification as from inspiring English sources

After all, prospective students arguably view that "four years or more" classification as an implication that they can graduate from any of these schools within four years.

The availability of many new phage genome sequences will enable a more concise classification, as well as the identification of many genetic markers.

This tells us that the classifier makes more classification errors as a fine-grained classifier than when used for classifying the three broad relation types, thus in turn verifying that task complexity increases with finer granularity of the relations.

In this section, we explore four methods that operate on features to build up a more representative model for classification as well as four methods that operate on distance between vectors to improve the performance.

Applying our method to study more cancers and more classification groups (such as stage, age, ethics, and sex) will give us further insight into the various pathogenesis mechanisms.

The general classification tool offers many more classification methods, such as Support Vector Machines (SVM), Linear Discriminant Analysis (LDA), and Naïve Bayes networks, but it does not allow classifying new samples like the KNN-classification does.

15 Originally designed as a treatment for obesity, it has seen extensive use as a pharmacological tool in assessing agonist actions through the CB1 receptor; however, the classification of this compound as a pure antagonist is likely to be misleading, and its more recent classification as a selective CB1-receptor inverse agonist is more accurate.

Despite an appearance that resembles hyaline cartilage, these tumors retain an epithelial phenotype and express specific chordoma markers, including cytokeratin and S-100, which are not found in cartilaginous tissue; this has suggested their alternative, more appropriate classification as "hyalinized chordomas" [ 22].

This enables us to make more representative classifications, as it might happen that a user 'A' always speaks very fast and loudly, while a user 'B' always speaks in a very relaxed way.

Results provided by the following: first column, K-means empirical distribution; second column, aspect model 1; third column, aspect model 2. The first three rows illustrate the case of a correctly identified marked natural image context by aspect model 2, resulting in a more accurate patch classification as compared to aspect model 1 and empirical distribution.

The fact that cigarette smoking has been increasingly condemned and tobacco advertising more regulated, despite classification as a legal drug just like alcohol, clearly shows that the negative influence of advertising can be completely eradicated if there is a firm commitment to do so.