Examples are presented for a classification system that utilizes multiple classification models to adapt to any desired set of raw data to be classified. The classification system may include multiple classification models stored in a model repository. A truth set of the raw data may be used to evaluate the fitness of each of the stored classification models. The models may be scored and ranked to determine which is the most appropriate to use for real time classification of the raw data. The optimal classification model may be used in a classification engine to classify the raw data in real time. This generates a classified output that may be interacted with by a user. A user interface may be used to permit feedback of the classified output to be generated. This feedback may then be transmitted to the offline system and recorded to further improve the classification models.
MULTI-MODAL DECLARATIVE CLASSIFICATION BASED ON UHRS, CLICK SIGNALS AND INTERPRETED DATA IN SEMANTIC CONVERSATIONAL UNDERSTANDING