| 摘要 |
A ranking in cascading learning system is described. The cascading learning system has a request analyzer, a request dispatcher and classifier, a search module, a terminology manager, and a cluster manager. The request analyzer receives a request for search terms from a client application and determines term context in the request to normalize request data from the term context. The normalized request data are classified and dispatched to a corresponding domain-specific module with a request dispatcher ranking calibrator. Each domain-specific module of a search module generates a prediction with a trained probability of an expected output using a corresponding domain-specific ranking calibrator. The terminology manager receives normalized request data from the request dispatcher and classifier, and manages terminology stored in a contextual network. The cluster manager comprises a central ranking calibrator, a training and sot container, and a module generator configured to generate a pluggable module. |
