Title :
Modular preference Moore machines in news mining agents
Author :
Wermter, Stefan ; Arevian, Garen
Author_Institution :
Inf. Centre, Sunderland Univ., UK
Abstract :
This paper focuses on hybrid symbolic neural architectures that support the task of classifying textual information in learning agents. We give an outline of these symbolic and neural preference Moore machines. Furthermore, we demonstrate how they can be used in the context of information mining and news classification. Using the Reuters newswire text data, we demonstrate how hybrid symbolic and neural machines can provide an effective foundation for learning news agents
Keywords :
data mining; information retrieval; learning (artificial intelligence); learning automata; Reuters newswire text data; hybrid symbolic neural architectures; information mining; learning agents; modular preference Moore machines; news classification; news mining agents; textual information classification; Data mining; Encoding; Informatics; Information retrieval; Internet; Machine learning; Manuals; Robustness; Routing; Web sites;
Conference_Titel :
IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-7078-3
DOI :
10.1109/NAFIPS.2001.943824
