Sentence examples for complementary classification from inspiring English sources

LMT are born out of the idea of combining two complementary classification schemes: linear logistic regression and tree induction.

MLP and RBF networks can be used in a double classification in such a way to take advantage from their complementary classification performances (with a confidence parameter to enhance classification rates) as well as from their competitive classification performances [3], MLP are neural global approximators, whereas RBF are neural local approximators [11].

3) that: MLP and RBF networks can be used in a double classification in such a way to take advantage from their complementary classification performances (with a confidence parameter to enhance classification rates) as well as from their competitive classification performances [3], MLP are neural global approximators, whereas RBF are neural local approximators [11].

But, as this classification is of minor help for diagnosis, ISSVD formulated in 2011 a complementary classification as an approach to clinical diagnosis [ 2].

In addition, the ACC/AHA Task Force has proposed a complementary classification that considers heart failure as a progressive disease (Table 2); patients are expected to progress from one stage to the next unless progression is slowed or stopped by medical treatment (Hunt et al. 2001).

As a result, complementary classifications are being proposed, like the predominant polarity (PP) based, which is defined as a clear tendency in the patient to present relapses in the manic or depressive poles.

To interpret functional changes in the dataset, we applied two pathway analysis programs, Metacore and GSEA, with different complementary pathway-classification properties.

These results could inform therapeutic decisions regarding TKI in lung cancer, and can provide a novel basis for tumor classification complementary to existing methods.

A complementary method of classification would be to group viral proteins according to their temporal profiles.

The frameworks of Bloom's taxonomy and the Delphi report provide useful complementary perspectives for classification of science-process skills.

A CBR system was selected for its capacity to handle enormous amounts of data, adapt to changes in the environment, and provide an adequate framework for integrating different complementary clustering and classification techniques.