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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 thedoi:10.1145/1273496.1273588 dblp:conf/icml/ParkerFT07 fatcat:ljf6h5dz6ncnvpfgmu3fhx43ky