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- W2951342632 abstract "We introduce an algorithm that, given n objects, learns a similarity matrix over all n^2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form object 'a' more similar to 'b' or to 'c'? and is chosen to be maximally informative given the preceding responses. The output is an embedding of the objects into Euclidean space (like MDS); we refer to this as the kernel. SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as striped among neckties and vowel vs. consonant among letters." @default.
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- W2951342632 date "2011-05-05" @default.
- W2951342632 modified "2023-10-02" @default.
- W2951342632 title "Adaptively Learning the Crowd Kernel" @default.
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