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For similarity search in high-dimensional vector spaces (or 'HDVSs'), researchers have proposed a number of new methods (or adaptations of existing methods) based, in the main, on data-space partitioning. However, the performance of these methods generally degrades as dimensionality increases. Although this phenomenon-known as the 'dimensional curse'-is(More)
This paper presents <italic>type classes</italic>, a new approach to <italic>ad-hoc</italic> polymorphism. Type classes permit overloading of arithmetic operators such as multiplication, and generalise the &#8220;eqtype variables&#8221; of Standard ML. Type classes extend the Hindley/Milner polymorphic type system, and provide a new approach to issues that(More)
Many similarity measures in multimedia databases and decision-support systems are based on underlying vector spaces of high dimensionality. Data-partitioning index methods for such spaces (for example, grid les, R-trees, and their variants) generally work well for low-dimensional spaces, but perform poorly as dimensionality increases. This problem has(More)
In TREC2004, Dublin City University took part in three tracks, Terabyte (in collaboration with University College Dublin), Genomic and Novelty. In this paper we will discuss each track separately and present separate conclusions from this work. In addition, we present a general description of a text retrieval engine that we have developed in the last year(More)