Conjunctoid learning and performance algorithms

R. J. Jannarone; K. F. Yu; Y. Takefuji

doi:10.1016/0893-6080(88)90223-7

Conjunctoid learning and performance algorithms

R. J. Jannarone, K. F. Yu, Y. Takefuji

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

Abstract

In its first 40 years the neural network learning (NNL) movement has produced an impressive array of learning models. We introduce a general family of fast and efficient NNL learning modules for binary events called 'conjunctoids', which employ an appropriate framework from probability theory; adapt a class of recently developed conjunctive models from psychometric theory; tailor sound statistical estimation and evaluation schemes to fit NNL learning needs; and allow VLSI implementations.

Original language	English
Pages (from-to)	186
Number of pages	1
Journal	Neural Networks
Volume	1
Issue number	1 SUPPL
DOIs	https://doi.org/10.1016/0893-6080(88)90223-7
Publication status	Published - 1988
Externally published	Yes
Event	International Neural Network Society 1988 First Annual Meeting - Boston, MA, USA Duration: 1988 Sept 6 → 1988 Sept 10

ASJC Scopus subject areas

Cognitive Neuroscience
Artificial Intelligence

Access to Document

10.1016/0893-6080(88)90223-7

Cite this

@article{fa5930bce2fd42ff82c68fe1ac9235e0,

title = "Conjunctoid learning and performance algorithms",

abstract = "In its first 40 years the neural network learning (NNL) movement has produced an impressive array of learning models. We introduce a general family of fast and efficient NNL learning modules for binary events called 'conjunctoids', which employ an appropriate framework from probability theory; adapt a class of recently developed conjunctive models from psychometric theory; tailor sound statistical estimation and evaluation schemes to fit NNL learning needs; and allow VLSI implementations.",

author = "Jannarone, {R. J.} and Yu, {K. F.} and Y. Takefuji",

year = "1988",

doi = "10.1016/0893-6080(88)90223-7",

language = "English",

volume = "1",

pages = "186",

journal = "Neural Networks",

issn = "0893-6080",

publisher = "Elsevier Limited",

number = "1 SUPPL",

note = "International Neural Network Society 1988 First Annual Meeting ; Conference date: 06-09-1988 Through 10-09-1988",

}

TY - JOUR

T1 - Conjunctoid learning and performance algorithms

AU - Jannarone, R. J.

AU - Yu, K. F.

AU - Takefuji, Y.

PY - 1988

Y1 - 1988

N2 - In its first 40 years the neural network learning (NNL) movement has produced an impressive array of learning models. We introduce a general family of fast and efficient NNL learning modules for binary events called 'conjunctoids', which employ an appropriate framework from probability theory; adapt a class of recently developed conjunctive models from psychometric theory; tailor sound statistical estimation and evaluation schemes to fit NNL learning needs; and allow VLSI implementations.

AB - In its first 40 years the neural network learning (NNL) movement has produced an impressive array of learning models. We introduce a general family of fast and efficient NNL learning modules for binary events called 'conjunctoids', which employ an appropriate framework from probability theory; adapt a class of recently developed conjunctive models from psychometric theory; tailor sound statistical estimation and evaluation schemes to fit NNL learning needs; and allow VLSI implementations.

UR - http://www.scopus.com/inward/record.url?scp=0024167310&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0024167310&partnerID=8YFLogxK

U2 - 10.1016/0893-6080(88)90223-7

DO - 10.1016/0893-6080(88)90223-7

M3 - Conference article

AN - SCOPUS:0024167310

SN - 0893-6080

VL - 1

SP - 186

JO - Neural Networks

JF - Neural Networks

IS - 1 SUPPL

T2 - International Neural Network Society 1988 First Annual Meeting

Y2 - 6 September 1988 through 10 September 1988

ER -

Conjunctoid learning and performance algorithms

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this