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The database for training and testing is prepared using finite element analysis.
We used 50%% of each iris database for training and the remaining 50%% for testing.
The issues discussed include the size of the database for training a neural network, database enlargement for training a neural network, and extrapolation based on modelling results.
Due to the lack of a large enough singing database for training the acoustic models of the recognizer, we first train models for speech and then adapt them linearly to singing.
The unbiased collection of URLs is reflected by its use in professional web page traffic-ranking services like Alexa (alexa.com) and as a database for training URL-based topic classification algorithms (Baykan, Henzinger, Marian, & Weber, 2011).
To investigate the color features, we built our own road database for training, which then results in giving the following probability density functions (PDF) of the red, green, and blue channels, as depicted in Fig. 2. The road candidate regions are estimated using Gaussian probabilities based on color channels.
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We used one of the four face databases for training and the rest three for tests.
Both JFA and i-vector however are techniques which require independent large databases for training the appropriate inter-speaker variability models.
Most of this work addresses the issues of feature selection, classifier parameter tuning, and the inherent difficulty in producing reliable "ground truth" databases for training and testing classifiers.
We have proved that for common speakers a cost-efficient cross-lingual adaptation can be done even with a training dataset smaller than the usual databases for training speech recognizers.
In order to test more the generalization of these results, we have tested the following: We used one of the four face databases for training and the rest three for tests.
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