Towards Efficient Active Learning of PDFA
Resumen:
We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations.
| 2022 | |
| Agencia Nacional de Investigación e Innovación | |
|
Artificial Intelligencece Active Learning Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
|
| Inglés | |
| Agencia Nacional de Investigación e Innovación | |
| REDI | |
| https://hdl.handle.net/20.500.12381/595 | |
| Acceso abierto | |
| Reconocimiento 4.0 Internacional. (CC BY) |
| _version_ | 1814959257963986944 |
|---|---|
| author | Mayr, F. |
| author2 | Yovine, S. Pan, F. Basset, N. Dang, T. |
| author2_role | author author author author |
| author_facet | Mayr, F. Yovine, S. Pan, F. Basset, N. Dang, T. |
| author_role | author |
| bitstream.checksum.fl_str_mv | 2d97768b1a25a7df5a347bb58fd2d77f 5099fdceeb28676b31c0d5541b74912f |
| bitstream.checksumAlgorithm.fl_str_mv | MD5 MD5 |
| bitstream.url.fl_str_mv | https://redi.anii.org.uy/jspui/bitstream/20.500.12381/595/2/license.txt https://redi.anii.org.uy/jspui/bitstream/20.500.12381/595/1/Towards_Efficient_Active_Learning_of_PDFA__short_.pdf |
| collection | REDI |
| dc.creator.none.fl_str_mv | Mayr, F. Yovine, S. Pan, F. Basset, N. Dang, T. |
| dc.date.accessioned.none.fl_str_mv | 2022-06-20T17:28:33Z |
| dc.date.available.none.fl_str_mv | 2022-06-20T17:28:33Z |
| dc.date.issued.none.fl_str_mv | 2022-06-17 |
| dc.description.abstract.none.fl_txt_mv | We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations. |
| dc.description.sponsorship.none.fl_txt_mv | Agencia Nacional de Investigación e Innovación |
| dc.identifier.anii.es.fl_str_mv | FMV_1_2019_1_155913 |
| dc.identifier.uri.none.fl_str_mv | https://hdl.handle.net/20.500.12381/595 |
| dc.language.iso.none.fl_str_mv | eng |
| dc.rights.es.fl_str_mv | Acceso abierto |
| dc.rights.license.none.fl_str_mv | Reconocimiento 4.0 Internacional. (CC BY) |
| dc.rights.none.fl_str_mv | info:eu-repo/semantics/openAccess |
| dc.source.none.fl_str_mv | reponame:REDI instname:Agencia Nacional de Investigación e Innovación instacron:Agencia Nacional de Investigación e Innovación |
| dc.subject.anii.none.fl_str_mv | Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
| dc.subject.es.fl_str_mv | Artificial Intelligencece Active Learning |
| dc.title.none.fl_str_mv | Towards Efficient Active Learning of PDFA |
| dc.type.es.fl_str_mv | Preprint |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/preprint |
| description | We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations. |
| eu_rights_str_mv | openAccess |
| format | preprint |
| id | REDI_533bd6159a5ef3e580dcba65b02d06b9 |
| identifier_str_mv | FMV_1_2019_1_155913 |
| instacron_str | Agencia Nacional de Investigación e Innovación |
| institution | Agencia Nacional de Investigación e Innovación |
| instname_str | Agencia Nacional de Investigación e Innovación |
| language | eng |
| network_acronym_str | REDI |
| network_name_str | REDI |
| oai_identifier_str | oai:redi.anii.org.uy:20.500.12381/595 |
| publishDate | 2022 |
| reponame_str | REDI |
| repository.mail.fl_str_mv | jmaldini@anii.org.uy |
| repository.name.fl_str_mv | REDI - Agencia Nacional de Investigación e Innovación |
| repository_id_str | 9421 |
| rights_invalid_str_mv | Reconocimiento 4.0 Internacional. (CC BY) Acceso abierto |
| spelling | Reconocimiento 4.0 Internacional. (CC BY)Acceso abiertoinfo:eu-repo/semantics/openAccess2022-06-20T17:28:33Z2022-06-20T17:28:33Z2022-06-17https://hdl.handle.net/20.500.12381/595FMV_1_2019_1_155913We propose a new active learning algorithm for PDFA based on three main aspects: a congruence over states which takes into account next-symbol probability distributions, a quantization that copes with differences in distributions, and an efficient tree-based data structure. Experiments showed significant performance gains with respect to reference implementations.Agencia Nacional de Investigación e InnovaciónengArtificial IntelligenceceActive LearningCiencias Naturales y ExactasCiencias de la Computación e InformaciónCiencias de la ComputaciónTowards Efficient Active Learning of PDFAPreprintinfo:eu-repo/semantics/preprint//Ciencias Naturales y Exactas/Ciencias de la Computación e Información/Ciencias de la Computaciónreponame:REDIinstname:Agencia Nacional de Investigación e Innovacióninstacron:Agencia Nacional de Investigación e InnovaciónMayr, F.Yovine, S.Pan, F.Basset, N.Dang, T.LICENSElicense.txtlicense.txttext/plain; charset=utf-84746https://redi.anii.org.uy/jspui/bitstream/20.500.12381/595/2/license.txt2d97768b1a25a7df5a347bb58fd2d77fMD52ORIGINALTowards_Efficient_Active_Learning_of_PDFA__short_.pdfTowards_Efficient_Active_Learning_of_PDFA__short_.pdfapplication/pdf434948https://redi.anii.org.uy/jspui/bitstream/20.500.12381/595/1/Towards_Efficient_Active_Learning_of_PDFA__short_.pdf5099fdceeb28676b31c0d5541b74912fMD5120.500.12381/5952022-06-20 14:28:35.031oai:redi.anii.org.uy:20.500.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- Agencia Nacional de Investigación e Innovaciónfalse |
| spellingShingle | Towards Efficient Active Learning of PDFA Mayr, F. Artificial Intelligencece Active Learning Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
| title | Towards Efficient Active Learning of PDFA |
| title_full | Towards Efficient Active Learning of PDFA |
| title_fullStr | Towards Efficient Active Learning of PDFA |
| title_full_unstemmed | Towards Efficient Active Learning of PDFA |
| title_short | Towards Efficient Active Learning of PDFA |
| title_sort | Towards Efficient Active Learning of PDFA |
| topic | Artificial Intelligencece Active Learning Ciencias Naturales y Exactas Ciencias de la Computación e Información Ciencias de la Computación |
| url | https://hdl.handle.net/20.500.12381/595 |
