| 摘要 |
<p>Features for training and run-time use of a prediction model are selected. A set of core features certain to be relevant are initially identified (2), as are a set of candidate features which are possibly relevant. In a first phase, a performance score is determined (5) for training vectors, each comprising the core features and one candidate feature. The candidate feature for the vector providing the best score is chosen (7). This feature is added to the set of core features, and a new phase is performed. New phases are commenced until there is no score improvement.</p> |
