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For the GFA model, thirty molecules were used as training set and eight as test set to evaluate the external predictability of the equations generated using GFA.
      
The training set was derived using twenty-eight compounds and six were used as test sets to evaluate external predictability of the model.
      
For the GRA model, thirteen molecules were used as training set and three as test set to evaluate the external predictability of the equations generated using GFA [r2 = 0.650 and r2pred = 0.807].
      
Both the approaches showed good correlation between experimental and observed biological activity in both training (r >amp;gt; 0.8) and test set (r =0.75 for 2D and r =0.68 for 3D QSAR model) with statistical significance >amp;gt; 99.5%.
      
A training set consisting of 23 compounds was selected to generate these hypotheses, and their activities were evaluated for haem polymerization inhibition and against chloroquine-sensitive (3D7) as well as chloroquine-resistant (K1) strains of p.
      
According to Elman networks' unique structure, a weight training algorithm is designed and a nonlinear adaptive controller is constructed.
      
OAS in training text is found by the combination of the FMM and BMM segmentation method.
      
The People Daily corpus of January 1998 is used in training and testing.
      
In the training phase, a novel solution is given to the discrete regularization framework defined on the graphs.
      
By artificially determining training samples, the NFPPG of Moershan National Forest Park was created.
      
Digitized visible models of these structures can be applied as a useful tool in clinical training.
      
This makes those models to be deficient in robustness, efficiency of learning, capacity, anti-jamming between training sets, and correlativity of samples, etc.
      
The rank regression problem studied in this paper is based on the training samples with uncertain labels, which often is the case for the manual estimated image labels.
      
For the training set, data on 150 complexes of a various nature are used, for which there exists an experimental estimate (Δ G).
      
The problem of identification (training) of the risk logic-and-probabilistic model from the statistical data was stated, and a training algorithm was set forth.
      
Both linear regression equations and nonlinear relationships obtained with the help of a neural network were shown to accurately predict mutagenic activity for the compounds structurally similar to those in the training sample.
      
Based on the Kohonen algorithm, a self-training neural network is constructed which allows one to classify geomagnetic disturbances using the data on parameters of the solar wind and interplanetary magnetic field.
      
Memory reproduction in rats treated with melipramine after training
      
Study of locomotion and central blood circulation in 9-to 14-year-old girls with various levels of physical training
      
Interhemispheric differences in cortical evoked activity in computer operating training
      
 

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