Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/3154
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | BOUTEN, Marcus | - |
dc.contributor.author | Reimers, L | - |
dc.contributor.author | VAN ROMPAEY, Bart | - |
dc.date.accessioned | 2007-11-26T16:16:29Z | - |
dc.date.available | 2007-11-26T16:16:29Z | - |
dc.date.issued | 1998 | - |
dc.identifier.citation | PHYSICAL REVIEW E, 58(2). p. 2378-2385 | - |
dc.identifier.issn | 1063-651X | - |
dc.identifier.uri | http://hdl.handle.net/1942/3154 | - |
dc.description.abstract | The learning problem for storing random patterns in a perceptron with binary weights can be facilitated by pretraining an appropriate precursor network with continuous weights. Unlike previous studies which compare the performance of different continuous-weight perceptrons on the hypersphere (spherical constraint), we also consider weight vectors constrained to the volume of the hypercube (cubical constraint). We compare the performance of the maximally stable networks on the hypersphere and in the hypercube, and show that the latter is superior for predicting the weights of the maximally stable binary perceptron. We further determine an upper bound for the fraction of binary weights that any precursor is able to predict correctly, and introduce a precursor in the hypercube that closely approaches this upper bound. We finally demonstrate the value of this hypercube precursor by carrying out simulations for a perceptron with up to 100 weights. | - |
dc.language.iso | en | - |
dc.publisher | AMERICAN PHYSICAL SOC | - |
dc.title | Learning in the hypercube: A stepping stone to the binary perceptron | - |
dc.type | Journal Contribution | - |
dc.identifier.epage | 2385 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | 2378 | - |
dc.identifier.volume | 58 | - |
local.format.pages | 8 | - |
dc.description.notes | Limburgs Univ Ctr, B-3590 Diepenbeek, Belgium.Bouten, M, Limburgs Univ Ctr, B-3590 Diepenbeek, Belgium. | - |
local.type.refereed | Refereed | - |
local.type.specified | Article | - |
dc.bibliographicCitation.oldjcat | A1 | - |
dc.identifier.doi | 10.1103/PhysRevE.58.2378 | - |
dc.identifier.isi | 000075381500046 | - |
item.accessRights | Closed Access | - |
item.contributor | BOUTEN, Marcus | - |
item.contributor | Reimers, L | - |
item.contributor | VAN ROMPAEY, Bart | - |
item.validation | ecoom 1999 | - |
item.fulltext | No Fulltext | - |
item.fullcitation | BOUTEN, Marcus; Reimers, L & VAN ROMPAEY, Bart (1998) Learning in the hypercube: A stepping stone to the binary perceptron. In: PHYSICAL REVIEW E, 58(2). p. 2378-2385. | - |
Appears in Collections: | Research publications |
SCOPUSTM
Citations
11
checked on Sep 3, 2020
WEB OF SCIENCETM
Citations
9
checked on Mar 13, 2024
Page view(s)
96
checked on Nov 7, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.