Harmonia loosely praestabilita: discovering adequate inductive strategies

Hilan Bensusan; Christophe Giraud-Carrier

Harmonia loosely praestabilita: discovering adequate inductive strategies

Hilan Bensusan
, Christophe Giraud-Carrier

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

Abstract

Landmarking is a novel approach to inductive model selection in Machine Learning. It uses simple, bare-bone inductive strategies to describe tasks and induce correlations between tasks and strategies. The paper presents the technique and reports experiments showing that landmarking performs well in a number of different scenarios. It also discusses the implications of landmarking to our understanding of inductive refinement.

Translated title of the contribution	Harmonia loosely praestabilita: discovering adequate inductive strategies
Original language	English
Title of host publication	Proceedings of the 22nd Annual Meeting of the Cognitive Science Society
Publisher	Cognitive Science Society
Pages	609 - 614
Number of pages	5
Publication status	Published - 2000

Bibliographical note

Other page information: 609-614
Conference Proceedings/Title of Journal: Proceedings of the 22nd Annual Meeting of the Cognitive Science Society
Other identifier: 1000501

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@inproceedings{86ae33cf3bfe48a5bdf2b2eec9840da6,

title = "Harmonia loosely praestabilita: discovering adequate inductive strategies",

abstract = "Landmarking is a novel approach to inductive model selection in Machine Learning. It uses simple, bare-bone inductive strategies to describe tasks and induce correlations between tasks and strategies. The paper presents the technique and reports experiments showing that landmarking performs well in a number of different scenarios. It also discusses the implications of landmarking to our understanding of inductive refinement. ",

author = "Hilan Bensusan and Christophe Giraud-Carrier",

note = "Other page information: 609-614 Conference Proceedings/Title of Journal: Proceedings of the 22nd Annual Meeting of the Cognitive Science Society Other identifier: 1000501",

year = "2000",

language = "English",

pages = "609 -- 614",

booktitle = "Proceedings of the 22nd Annual Meeting of the Cognitive Science Society",

publisher = "Cognitive Science Society",

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T1 - Harmonia loosely praestabilita: discovering adequate inductive strategies

AU - Bensusan, Hilan

AU - Giraud-Carrier, Christophe

N1 - Other page information: 609-614 Conference Proceedings/Title of Journal: Proceedings of the 22nd Annual Meeting of the Cognitive Science Society Other identifier: 1000501

PY - 2000

Y1 - 2000

N2 - Landmarking is a novel approach to inductive model selection in Machine Learning. It uses simple, bare-bone inductive strategies to describe tasks and induce correlations between tasks and strategies. The paper presents the technique and reports experiments showing that landmarking performs well in a number of different scenarios. It also discusses the implications of landmarking to our understanding of inductive refinement.

AB - Landmarking is a novel approach to inductive model selection in Machine Learning. It uses simple, bare-bone inductive strategies to describe tasks and induce correlations between tasks and strategies. The paper presents the technique and reports experiments showing that landmarking performs well in a number of different scenarios. It also discusses the implications of landmarking to our understanding of inductive refinement.

M3 - Conference Contribution (Conference Proceeding)

SP - 609

EP - 614

BT - Proceedings of the 22nd Annual Meeting of the Cognitive Science Society

PB - Cognitive Science Society

ER -

Harmonia loosely praestabilita: discovering adequate inductive strategies

Abstract

Bibliographical note

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