Scaling relationships in back-propagation learning
Complex Systems
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
Multi-agent learning approach to WWW information retrieval using neural network
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
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
Internet fish
The SMART Retrieval System—Experiments in Automatic Document Processing
The SMART Retrieval System—Experiments in Automatic Document Processing
When one sample is not enough: improving text database selection using shrinkage
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Classification-aware hidden-web text database selection
ACM Transactions on Information Systems (TOIS)
International Journal of Information Technology Project Management
International Journal of Decision Support System Technology
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As large numbers of text databases have become available on the Web, many efforts have been made to solve the text database discovery problem: finding which text databases (out of many candidates) are most likely to provide relevant documents to a given query. In this paper, we propose a neural net based approach to this problem. First, we present a neural net agent that learns about underlying text databases from the user's relevance feedback. For a given query, the neural net agent, which is sufficiently trained on the basis of the backpropagation 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 net 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 statistical approaches.