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Learning for efficient retrieval of structured data with noisy queries

TitleLearning for efficient retrieval of structured data with noisy queries
Publication TypeConference Paper
Year of Publication2007
AuthorsParker, C., A. Fern, and P. Tadepalli
Conference NameInternational Conference on Machine Learning (ICML-2007)
Pagination729-736
Date Published06/2007
Conference LocationCorvallis, Oregon
Abstract

Increasingly large collections of structured data necessitate the development of efficient, noise-tolerant retrieval tools. In this work, we consider this issue and describe an approach to learn a similarity function that is not only accurate, but that also increases the effectiveness of retrieval data structures. We present an algorithm that uses functional gradient boosting to maximize both retrieval accuracy and the retrieval efficiency of vantage point trees. We demonstrate the effectiveness of our approach on two datasets, including a moderately sized real-world dataset of folk music.

DOI10.1145/1273496.1273588