1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPBW34M/3A32SLE |
Repository | sid.inpe.br/sibgrapi/2011/07.06.23.53 |
Last Update | 2011:07.06.23.53.51 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2011/07.06.23.53.51 |
Metadata Last Update | 2022:06.14.00.07.08 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2011.21 |
Citation Key | RauberBern:2011:KeMuPe |
Title | Kernel Multilayer Perceptron |
Format | DVD, On-line. |
Year | 2011 |
Access Date | 2024, May 21 |
Number of Files | 1 |
Size | 154 KiB |
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2. Context | |
Author | 1 Rauber, Thomas W. 2 Berns, Karsten |
Affiliation | 1 Departamento de Informática, Centro Tecnológico, Universidade Federal do Espírito Santo 2 Robotics Research Lab, Department of Computer Science, University of Kaiserslautern, Gottlieb-Daimler-Strasse, 67663 Kaiserslautern, Germany |
Editor | Lewiner, Thomas Torres, Ricardo |
e-Mail Address | thomas@inf.ufes.br |
Conference Name | Conference on Graphics, Patterns and Images, 24 (SIBGRAPI) |
Conference Location | Maceió, AL, Brazil |
Date | 28-31 Aug. 2011 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full Paper |
History (UTC) | 2011-07-23 15:36:12 :: thomas@inf.ufes.br -> administrator :: 2011 2022-06-14 00:07:08 :: administrator -> :: 2011 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | Multilayer Perceptron kernel mapping |
Abstract | We enhance the Multilayer Perceptron to map a feature vector not only from the original d-dimensional feature space, but from an intermediate implicit Hilbert feature space in which kernels calculate inner products. The kernel substitutes the usual inner product between weight vectors and the input vector (or the feature vector of the hidden layer). The objective is to boost the generalization capability of this universal function approximator even more. Classification experiments with standard Machine Learning data sets are shown. We are able to improve the classification accuracy performance criterion for certain kernel types and their intrinsic parameters for the majority of the data sets. |
Arrangement 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2011 > Kernel Multilayer Perceptron |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > Kernel Multilayer Perceptron |
doc Directory Content | access |
source Directory Content | there are no files |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPBW34M/3A32SLE |
zipped data URL | http://urlib.net/zip/8JMKD3MGPBW34M/3A32SLE |
Language | en |
Target File | 86589.pdf |
User Group | thomas@inf.ufes.br |
Visibility | shown |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/46SKNPE 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2022/05.15.00.56 4 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume |
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