Consistency in a model for distributed learning with specialists

Joel B. Predd; Sanjeev R. Kulkarni; H. Vincent Poor

Consistency in a model for distributed learning with specialists

Joel B. Predd, Sanjeev R. Kulkarni, H. Vincent Poor

Research output: Contribution to journal › Conference article › peer-review

Abstract

Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.

Original language	English (US)
Pages (from-to)	465
Number of pages	1
Journal	IEEE International Symposium on Information Theory - Proceedings
State	Published - 2004
Event	Proceedings - 2004 IEEE International Symposium on Information Theory - Chicago, IL, United States Duration: Jun 27 2004 → Jul 2 2004

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Information Systems
Modeling and Simulation
Applied Mathematics

Cite this

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title = "Consistency in a model for distributed learning with specialists",

abstract = "Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.",

author = "Predd, {Joel B.} and Kulkarni, {Sanjeev R.} and Poor, {H. Vincent}",

year = "2004",

language = "English (US)",

pages = "465",

journal = "IEEE International Symposium on Information Theory - Proceedings",

issn = "2157-8097",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "Proceedings - 2004 IEEE International Symposium on Information Theory ; Conference date: 27-06-2004 Through 02-07-2004",

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T1 - Consistency in a model for distributed learning with specialists

AU - Predd, Joel B.

AU - Kulkarni, Sanjeev R.

AU - Poor, H. Vincent

PY - 2004

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N2 - Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.

AB - Motivated by sensor networks and traditional methods of statistical pattern recognition, a model for distributed learning is formulated. The model is in line with learning models considered in the context of Stone-type classifiers, but differs in the dependency structure of the sampling process; questions of universal consistency are addressed.

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M3 - Conference article

AN - SCOPUS:5044246259

SN - 2157-8097

SP - 465

JO - IEEE International Symposium on Information Theory - Proceedings

JF - IEEE International Symposium on Information Theory - Proceedings

T2 - Proceedings - 2004 IEEE International Symposium on Information Theory

Y2 - 27 June 2004 through 2 July 2004

ER -

Consistency in a model for distributed learning with specialists

Abstract

All Science Journal Classification (ASJC) codes

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