Introduction to the theory of neural computation
Introduction to the theory of neural computation
Neurocomputing: foundations of research
Neural networks: algorithms, applications, and programming techniques
Neural networks: algorithms, applications, and programming techniques
The effectiveness of GIOSS for the text database discovery problem
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Machine Learning as an Experimental Science
Machine Learning
Information Retrieval on the World Wide Web
IEEE Internet Computing
Hierarchically Classifying Documents Using Very Few Words
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Determining Text Databases to Search in the Internet
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Generalizing GlOSS to Vector-Space Databases and Broker Hierarchies
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
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As the number and diversity of text databases on the Internet increases rapidly, users are faced with finding the text databases that are relevant to the user query. Identifying the relevant text databases out of many candidates for a given query is called the text database discovery problem. In this paper, we propose a novel approach, a neural approach, to the text database discovery problem. First, we present a neural agent that learns about underlying text databases from the user's relevance feedback. For a given query, the neural agent, which is sufficiently trained on the basis of the neural net learning mechanism, discovers the text databases associated with the relevant documents and retrieves those documents effectively. In order to scale our approach with the large number of text databases, we also propose the hierarchical organization of neural agents which reduces the total training cost at the acceptable level. Finally, we evaluate the performance of our approach by comparing it to those of the conventional well-known approaches.