Learning using privileged information in prototype based models

Shereen Fouad; Peter Tino; Somak Raychaudhury; Petra Schneider

doi:10.1007/978-3-642-33266-1_40

Learning using privileged information in prototype based models

Shereen Fouad^*, Peter Tino, Somak Raychaudhury, Petra Schneider

^*Corresponding author for this work

University of Birmingham

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

9 Citations (Scopus)

Original language	English
Title of host publication	Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings
Pages	322-329
Number of pages	8
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-33266-1_40
Publication status	Published (VoR) - 2012
Event	22nd International Conference on Artificial Neural Networks, ICANN 2012 - Lausanne, Switzerland Duration: 11 Sept 2012 → 14 Sept 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	7553 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	22nd International Conference on Artificial Neural Networks, ICANN 2012
Country/Territory	Switzerland
City	Lausanne
Period	11/09/12 → 14/09/12

Keywords

Generalized Matrix Learning Vector Quantization (GMLVQ)
Information Theoretic Metric Learning (ITML)
Learning Using Hidden Information (LUHI)

Access to Document

10.1007/978-3-642-33266-1_40

Cite this

Fouad, S., Tino, P., Raychaudhury, S., & Schneider, P. (2012). Learning using privileged information in prototype based models. In Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings (PART 2 ed., pp. 322-329). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7553 LNCS, No. PART 2). https://doi.org/10.1007/978-3-642-33266-1_40

Fouad, Shereen ; Tino, Peter ; Raychaudhury, Somak et al. / Learning using privileged information in prototype based models. Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2. ed. 2012. pp. 322-329 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{f93a9c2776614737962c8c7d499441f4,

title = "Learning using privileged information in prototype based models",

keywords = "Generalized Matrix Learning Vector Quantization (GMLVQ), Information Theoretic Metric Learning (ITML), Learning Using Hidden Information (LUHI)",

author = "Shereen Fouad and Peter Tino and Somak Raychaudhury and Petra Schneider",

year = "2012",

doi = "10.1007/978-3-642-33266-1_40",

language = "English",

isbn = "9783642332654",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "322--329",

booktitle = "Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings",

edition = "PART 2",

note = "22nd International Conference on Artificial Neural Networks, ICANN 2012 ; Conference date: 11-09-2012 Through 14-09-2012",

}

Fouad, S, Tino, P, Raychaudhury, S & Schneider, P 2012, Learning using privileged information in prototype based models. in Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 7553 LNCS, pp. 322-329, 22nd International Conference on Artificial Neural Networks, ICANN 2012, Lausanne, Switzerland, 11/09/12. https://doi.org/10.1007/978-3-642-33266-1_40

Learning using privileged information in prototype based models. / Fouad, Shereen; Tino, Peter; Raychaudhury, Somak et al.
Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2. ed. 2012. p. 322-329 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7553 LNCS, No. PART 2).

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

TY - GEN

T1 - Learning using privileged information in prototype based models

AU - Fouad, Shereen

AU - Tino, Peter

AU - Raychaudhury, Somak

AU - Schneider, Petra

PY - 2012

Y1 - 2012

KW - Generalized Matrix Learning Vector Quantization (GMLVQ)

KW - Information Theoretic Metric Learning (ITML)

KW - Learning Using Hidden Information (LUHI)

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UR - http://www.scopus.com/inward/citedby.url?scp=84867664602&partnerID=8YFLogxK

U2 - 10.1007/978-3-642-33266-1_40

DO - 10.1007/978-3-642-33266-1_40

M3 - Conference contribution

AN - SCOPUS:84867664602

SN - 9783642332654

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 322

EP - 329

BT - Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings

T2 - 22nd International Conference on Artificial Neural Networks, ICANN 2012

Y2 - 11 September 2012 through 14 September 2012

ER -

Fouad S, Tino P, Raychaudhury S, Schneider P. Learning using privileged information in prototype based models. In Artificial Neural Networks and Machine Learning, ICANN 2012 - 22nd International Conference on Artificial Neural Networks, Proceedings. PART 2 ed. 2012. p. 322-329. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-33266-1_40

Learning using privileged information in prototype based models

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Keywords

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