Semi-supervised learning with trees

Charles Kemp; Thomas L. Griffiths; Sean Stromsten; Joshua B. Tenenbaum

Semi-supervised learning with trees

Charles Kemp, Thomas L. Griffiths, Sean Stromsten, Joshua B. Tenenbaum

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets.

Original language	English (US)
Title of host publication	Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003
Publisher	Neural information processing systems foundation
ISBN (Print)	0262201526, 9780262201520
State	Published - 2004
Externally published	Yes
Event	17th Annual Conference on Neural Information Processing Systems, NIPS 2003 - Vancouver, BC, Canada Duration: Dec 8 2003 → Dec 13 2003

Publication series

Name	Advances in Neural Information Processing Systems
ISSN (Print)	1049-5258

Other

Other	17th Annual Conference on Neural Information Processing Systems, NIPS 2003
Country/Territory	Canada
City	Vancouver, BC
Period	12/8/03 → 12/13/03

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Information Systems
Signal Processing

Cite this

@inproceedings{d7397ff5a36a48a5b205ff146b404b7d,

title = "Semi-supervised learning with trees",

abstract = "We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets.",

author = "Charles Kemp and Griffiths, {Thomas L.} and Sean Stromsten and Tenenbaum, {Joshua B.}",

year = "2004",

language = "English (US)",

isbn = "0262201526",

series = "Advances in Neural Information Processing Systems",

publisher = "Neural information processing systems foundation",

booktitle = "Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003",

note = "17th Annual Conference on Neural Information Processing Systems, NIPS 2003 ; Conference date: 08-12-2003 Through 13-12-2003",

}

Kemp, C, Griffiths, TL, Stromsten, S & Tenenbaum, JB 2004, Semi-supervised learning with trees. in Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Advances in Neural Information Processing Systems, Neural information processing systems foundation, 17th Annual Conference on Neural Information Processing Systems, NIPS 2003, Vancouver, BC, Canada, 12/8/03.

Semi-supervised learning with trees. / Kemp, Charles; Griffiths, Thomas L.; Stromsten, Sean et al.
Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003. Neural information processing systems foundation, 2004. (Advances in Neural Information Processing Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Semi-supervised learning with trees

AU - Kemp, Charles

AU - Griffiths, Thomas L.

AU - Stromsten, Sean

AU - Tenenbaum, Joshua B.

PY - 2004

Y1 - 2004

N2 - We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets.

AB - We describe a nonparametric Bayesian approach to generalizing from few labeled examples, guided by a larger set of unlabeled objects and the assumption of a latent tree-structure to the domain. The tree (or a distribution over trees) may be inferred using the unlabeled data. A prior over concepts generated by a mutation process on the inferred tree(s) allows efficient computation of the optimal Bayesian classification function from the labeled examples. We test our approach on eight real-world datasets.

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UR - http://www.scopus.com/inward/citedby.url?scp=84899010991&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84899010991

SN - 0262201526

SN - 9780262201520

T3 - Advances in Neural Information Processing Systems

BT - Advances in Neural Information Processing Systems 16 - Proceedings of the 2003 Conference, NIPS 2003

PB - Neural information processing systems foundation

T2 - 17th Annual Conference on Neural Information Processing Systems, NIPS 2003

Y2 - 8 December 2003 through 13 December 2003

ER -

Semi-supervised learning with trees

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

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