Adaptively learning the crowd kernel

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Standard

Adaptively learning the crowd kernel. / Tamuz, Omer; Liu, Ce; Belongie, Serge; Shamir, Ohad; Kalai, Adam Tauman.

I: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 2011, s. 673-680.

Publikation: Bidrag til tidsskrift › Konferenceartikel › Forskning › fagfællebedømt

Harvard

Tamuz, O, Liu, C, Belongie, S, Shamir, O & Kalai, AT 2011, 'Adaptively learning the crowd kernel', Proceedings of the 28th International Conference on Machine Learning, ICML 2011, s. 673-680.

APA

Tamuz, O., Liu, C., Belongie, S., Shamir, O., & Kalai, A. T. (2011). Adaptively learning the crowd kernel. Proceedings of the 28th International Conference on Machine Learning, ICML 2011, 673-680.

Vancouver

Tamuz O, Liu C, Belongie S, Shamir O, Kalai AT. Adaptively learning the crowd kernel. Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 2011;673-680.

Author

Tamuz, Omer ; Liu, Ce ; Belongie, Serge ; Shamir, Ohad ; Kalai, Adam Tauman. / Adaptively learning the crowd kernel. I: Proceedings of the 28th International Conference on Machine Learning, ICML 2011. 2011 ; s. 673-680.

Bibtex

@inproceedings{766b0c5af3834a63bc14e506fb7a39ce,
title = "Adaptively learning the crowd kernel",
abstract = "We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form {"}is 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 {"}crowd kernel.{"} SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as {"}is striped{"} among neckties and {"}vowel vs. consonant{"} among letters.",
author = "Omer Tamuz and Ce Liu and Serge Belongie and Ohad Shamir and Kalai, {Adam Tauman}",
year = "2011",
language = "English",
pages = "673--680",
journal = "Proceedings of the 28th International Conference on Machine Learning, ICML 2011",
note = "28th International Conference on Machine Learning, ICML 2011 ; Conference date: 28-06-2011 Through 02-07-2011",

}

RIS

TY - GEN

T1 - Adaptively learning the crowd kernel

AU - Tamuz, Omer

AU - Liu, Ce

AU - Belongie, Serge

AU - Shamir, Ohad

AU - Kalai, Adam Tauman

PY - 2011

Y1 - 2011

N2 - We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is 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 "crowd kernel." SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as "is striped" among neckties and "vowel vs. consonant" among letters.

AB - We introduce an algorithm that, given n objects, learns a similarity matrix over all n2 pairs, from crowdsourced data alone. The algorithm samples responses to adaptively chosen triplet-based relative-similarity queries. Each query has the form "is 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 "crowd kernel." SVMs reveal that the crowd kernel captures prominent and subtle features across a number of domains, such as "is striped" among neckties and "vowel vs. consonant" among letters.

UR - http://www.scopus.com/inward/record.url?scp=80053456767&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:80053456767

SP - 673

EP - 680

JO - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

JF - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

T2 - 28th International Conference on Machine Learning, ICML 2011

Y2 - 28 June 2011 through 2 July 2011

ER -

ID: 301831017