For each sequence, an AAM was trained based on exactly one stride, using the provided landmark data.
As an illustration, for the traditional classification-based QSAR models, they are trained based on a set of molecules with known classification labels for a given target.
Since QRNN is trained based on temporally simultaneous features, it cannot be utilized directly in forecasting one year ahead because some features, like hourly temperature in the next year, cannot be foreseen.
Essentially, the networks are trained based on parallel sets of mixtures and their constituent target sources.
In the supervised approach a classification model is typically trained based on labelled training documents.
A single model was trained based on activity data from ChEMBL exclusively.
Various NN structures were trained based on the back-propagation feed-forward approach.
100Credit's credit scoring model is trained based on default cases and creditable borrowers provided from multiple different financial institutions.
Classifiers trained based on a similar approach have been developed for detecting newsworthiness and credibility of tweets [7].
Another model has been created for sentiment analysis of tweets, which was trained based on hashtags and emoticons [44].
The neural network is then trained based on the CFD analysis results for the prediction of hydrodynamic loads.
