A Working Set Algorithm for Accelerating the Relevance Vector Machine

David Ben-Shimon; Armin Shmilovici

A Working Set Algorithm for Accelerating the Relevance Vector Machine

Department of Software and Information Systems Engineering

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

Abstract

The runtime complexity of the Relevance Vector Machine (RVM) is which makes it too expensive for moderately sized machine learning problems. We propose a working set algorithm which reduces the runtime complexity to O(N²). Experiments verify the viability of the method for classification benchmark problemsץ

Original language	English
Title of host publication	Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU'2006), Proceedings Poster Sessions pp. 56-59, Paris, France, July 2-7
Pages	56-59
State	Published - 2006

Keywords

Machine Learning
Data Mining
The Relevance Vector Machine
Working Set

Cite this

@inproceedings{7af4cfc1df3840919233ff5d6af604ca,

title = "A Working Set Algorithm for Accelerating the Relevance Vector Machine",

abstract = "The runtime complexity of the Relevance Vector Machine (RVM) is which makes it too expensive for moderately sized machine learning problems. We propose a working set algorithm which reduces the runtime complexity to O(N2). Experiments verify the viability of the method for classification benchmark problemsץ",

keywords = "Machine Learning, Data Mining, The Relevance Vector Machine, Working Set",

author = "David Ben-Shimon and Armin Shmilovici",

year = "2006",

language = "English",

pages = "56--59",

booktitle = "Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU'2006), Proceedings Poster Sessions pp. 56-59, Paris, France, July 2-7",

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A Working Set Algorithm for Accelerating the Relevance Vector Machine. / Ben-Shimon, David; Shmilovici, Armin.
Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU'2006), Proceedings Poster Sessions pp. 56-59, Paris, France, July 2-7. 2006. p. 56-59.

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

TY - GEN

T1 - A Working Set Algorithm for Accelerating the Relevance Vector Machine

AU - Ben-Shimon, David

AU - Shmilovici, Armin

PY - 2006

Y1 - 2006

N2 - The runtime complexity of the Relevance Vector Machine (RVM) is which makes it too expensive for moderately sized machine learning problems. We propose a working set algorithm which reduces the runtime complexity to O(N2). Experiments verify the viability of the method for classification benchmark problemsץ

AB - The runtime complexity of the Relevance Vector Machine (RVM) is which makes it too expensive for moderately sized machine learning problems. We propose a working set algorithm which reduces the runtime complexity to O(N2). Experiments verify the viability of the method for classification benchmark problemsץ

KW - Machine Learning

KW - Data Mining

KW - The Relevance Vector Machine

KW - Working Set

M3 - Conference contribution

SP - 56

EP - 59

BT - Proceedings of the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge Based Systems (IPMU'2006), Proceedings Poster Sessions pp. 56-59, Paris, France, July 2-7

ER -

A Working Set Algorithm for Accelerating the Relevance Vector Machine

Abstract

Keywords

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Cite this