Constructive neural network learning algorithms

Rajesh Parekh; Jihoon Yang; Vasant Honavar

Constructive neural network learning algorithms

Rajesh Parekh, Jihoon Yang, Vasant Honavar

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

Abstract

Constructive algorithms can increase the construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms have a set of inductive and representational biases implicit in the design choices that determine where to add neurons and how to train and prune them. The biases that best suit the needs of each individual classification tasks are then systematically characterized.

Original language	English (US)
Title of host publication	Proceedings of the National Conference on Artificial Intelligence
Editors	Anon
Publisher	AAAI
Pages	1398
Number of pages	1
Volume	2
State	Published - 1996
Event	Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) - Portland, OR, USA Duration: Aug 4 1996 → Aug 8 1996

Other

Other	Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2)
City	Portland, OR, USA
Period	8/4/96 → 8/8/96

All Science Journal Classification (ASJC) codes

Software
Artificial Intelligence

Cite this

@inproceedings{9b4c6372119b49df9c9d00754720f814,

title = "Constructive neural network learning algorithms",

abstract = "Constructive algorithms can increase the construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms have a set of inductive and representational biases implicit in the design choices that determine where to add neurons and how to train and prune them. The biases that best suit the needs of each individual classification tasks are then systematically characterized.",

author = "Rajesh Parekh and Jihoon Yang and Vasant Honavar",

year = "1996",

language = "English (US)",

volume = "2",

pages = "1398",

editor = "Anon",

booktitle = "Proceedings of the National Conference on Artificial Intelligence",

publisher = "AAAI",

note = "Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2) ; Conference date: 04-08-1996 Through 08-08-1996",

}

TY - GEN

T1 - Constructive neural network learning algorithms

AU - Parekh, Rajesh

AU - Yang, Jihoon

AU - Honavar, Vasant

PY - 1996

Y1 - 1996

N2 - Constructive algorithms can increase the construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms have a set of inductive and representational biases implicit in the design choices that determine where to add neurons and how to train and prune them. The biases that best suit the needs of each individual classification tasks are then systematically characterized.

AB - Constructive algorithms can increase the construction of potentially minimal neural network architectures for pattern classification tasks. These algorithms have a set of inductive and representational biases implicit in the design choices that determine where to add neurons and how to train and prune them. The biases that best suit the needs of each individual classification tasks are then systematically characterized.

UR - http://www.scopus.com/inward/record.url?scp=0030349502&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0030349502&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:0030349502

VL - 2

SP - 1398

BT - Proceedings of the National Conference on Artificial Intelligence

A2 - Anon, null

PB - AAAI

T2 - Proceedings of the 1996 13th National Conference on Artificial Intelligence. Part 2 (of 2)

Y2 - 4 August 1996 through 8 August 1996

ER -

Constructive neural network learning algorithms

Abstract

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All Science Journal Classification (ASJC) codes

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Cite this