Interactive document retrieval with relational learning
Proceedings of the 2001 ACM symposium on Applied computing
Modern Information Retrieval
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Relevance Feedback using Support Vector Machines
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Support vector machine active learning with applications to text classification
The Journal of Machine Learning Research
Learning ontology for personalized video retrieval
Workshop on multimedia information retrieval on The many faces of multimedia semantics
SVM-based interactive document retrieval with active learning
New Generation Computing
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This paper describes an application of SVM(Support Vector Machines) to interactive document retrieval using active learning. Some works have been done to apply classification learning like SVM to relevance feedback and obtained successful results. However they did not fully utilize characteristic of example distribution in document retrieval. We propose heuristics to bias document showing according to distribution of examples in document retrieval. This heuristic is executed by selecting examples to show a user in neighbors of positive support vectors, and it improves learning efficiency. We implemented a SVM-based interactive document retrieval system using our proposed heuristic, and compare it with conventional systems like Rocchio-based system and a SVM-based system without the heuristic. We conducted systematic experiments using large data sets including over 500,000 paper articles and confirmed our system outperformed other ones.