Conversing learning: applying the wisdom of crowds to assist never ending learning tasks
Autor(a) principal: | |
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Data de Publicação: | 2013 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFSCAR |
Texto Completo: | https://repositorio.ufscar.br/handle/ufscar/14524 |
Resumo: | In Machine Learning systems we often apply techniques are often applied to learn about real world problems behavior from data. Traditionally, such data comes from instances of datasets (that represents the target problem) which we want to learn from. This approach has been broadly used in many different application domains such as recommending systems, meteorological prediction, medical diagnosis, etc. The recent years of quick development of communications technology, that made the Internet faster and available, made possible the acquisition of information to feed a growing number of Machine Learning applications and, in addition, brought light to the use of human computation and crowdsourcing approaches commonly applied to problems that are easy for human but difficult for computers. Thus, the Social Web has been the focus of many research in Artificial Intelligence and Machine Learning. In this work we want to show how we can take advantage from the Social Web to add value to Machine Learning systems which can actively and autonomously ask for web users help to improve learning performance. This work proposes a model of learning called Conversing Learning that is is based on both, Active Learning and Interactive Learning, and is intended to allow machines to convert its knowledge base into human understandable content and then, actively and autonomously ask people (Web users) to take part into the knowledge acquisition (and labeling) process. The work presents how to apply Conversing Learning tasks to assist Machine Learning tasks, and discusses the success of experiments exploring the subtleties of this model. |
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Pedro, Saulo Domingos de SouzaHruschka Júnior, Estevam Rafaelhttp://lattes.cnpq.br/2097340857065853http://lattes.cnpq.br/49844139068187531a76caac-d109-4eed-bf1a-0761a1b1fb542021-07-06T11:47:01Z2021-07-06T11:47:01Z2013-09-21PEDRO, Saulo Domingos de Souza. Conversing learning: applying the wisdom of crowds to assist never ending learning tasks. 2013. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2013. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14524.https://repositorio.ufscar.br/handle/ufscar/14524In Machine Learning systems we often apply techniques are often applied to learn about real world problems behavior from data. Traditionally, such data comes from instances of datasets (that represents the target problem) which we want to learn from. This approach has been broadly used in many different application domains such as recommending systems, meteorological prediction, medical diagnosis, etc. The recent years of quick development of communications technology, that made the Internet faster and available, made possible the acquisition of information to feed a growing number of Machine Learning applications and, in addition, brought light to the use of human computation and crowdsourcing approaches commonly applied to problems that are easy for human but difficult for computers. Thus, the Social Web has been the focus of many research in Artificial Intelligence and Machine Learning. In this work we want to show how we can take advantage from the Social Web to add value to Machine Learning systems which can actively and autonomously ask for web users help to improve learning performance. This work proposes a model of learning called Conversing Learning that is is based on both, Active Learning and Interactive Learning, and is intended to allow machines to convert its knowledge base into human understandable content and then, actively and autonomously ask people (Web users) to take part into the knowledge acquisition (and labeling) process. The work presents how to apply Conversing Learning tasks to assist Machine Learning tasks, and discusses the success of experiments exploring the subtleties of this model.Nos sistemas de aprendizado de máquina, geralmente aplicamos técnicas para aprender sobre os problemas do mundo real a partir dos dados. Tradicionalmente, esses dados são provenientes de instâncias de conjuntos de dados (que representam o problema do destino) com as quais queremos aprender. Essa abordagem tem sido amplamente utilizada em vários domínios de aplicativos, como recomendação de sistemas, previsão meteorológica, diagnóstico médico etc. Os últimos anos de rápido desenvolvimento da tecnologia de comunicações, que tornaram a Internet mais rápida e disponível, possibilitaram a aquisição de informações para alimentar um número crescente de aplicativos de Machine Learning e, além disso, trouxe luz ao uso de abordagens de computação e crowdsourcing humano comumente aplicadas a problemas fáceis para humanos, mas difíceis para computadores. Assim, a Web Social tem sido o foco de muitas pesquisas em Inteligência Artificial e Machine Learning. Neste trabalho, queremos mostrar como podemos tirar proveito da Web Social para agregar valor aos sistemas de Machine Learning, que podem solicitar de forma ativa e autônoma os usuários da Web que ajudam a melhorar o desempenho da aprendizagem. Este trabalho propõe um modelo de aprendizado chamado Conversing Learning, que se baseia tanto em Aprendizado Ativo quanto em Aprendizado Interativo, e visa permitir que as máquinas convertam sua base de conhecimento em conteúdo humano compreensível e, em seguida, solicite de forma ativa e autônoma às pessoas (usuários da Web) participar do processo de aquisição (e rotulagem) de conhecimento. O trabalho apresenta como aplicar as tarefas do Conversing Learning para ajudar nas tarefas de Machine Learning e discute o sucesso de experimentos que exploram as sutilezas desse modelo.Não recebi financiamentoengUniversidade Federal de São CarlosCâmpus São CarlosPrograma de Pós-Graduação em Ciência da Computação - PPGCCUFSCarAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessNever ending learningConversing learningSocial WebCIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAOConversing learning: applying the wisdom of crowds to assist never ending learning tasksConversing learning: aplicando o conhecimento coletivo para auxiliar tarefas de aprendizado sem fiminfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesis6c142165-1935-4e21-8c88-f27f8c42b0c1reponame:Repositório Institucional da UFSCARinstname:Universidade Federal de São Carlos (UFSCAR)instacron:UFSCARORIGINALSauloPedro.pdfSauloPedro.pdfDocumento da dissertaçãoapplication/pdf1188740https://repositorio.ufscar.br/bitstream/ufscar/14524/4/SauloPedro.pdfca0fecf16ae904dcdc98b80a02b23b93MD54carta_comprovante.pdfcarta_comprovante.pdfCarta comprovante assinada pelo orientadorapplication/pdf93618https://repositorio.ufscar.br/bitstream/ufscar/14524/2/carta_comprovante.pdfaee83ab4ff6dc96fa6105d19e5e49f98MD52CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8811https://repositorio.ufscar.br/bitstream/ufscar/14524/5/license_rdfe39d27027a6cc9cb039ad269a5db8e34MD55TEXTSauloPedro.pdf.txtSauloPedro.pdf.txtExtracted texttext/plain127215https://repositorio.ufscar.br/bitstream/ufscar/14524/6/SauloPedro.pdf.txt67f99e947cd3775a1e3453653a7c8dadMD56carta_comprovante.pdf.txtcarta_comprovante.pdf.txtExtracted texttext/plain1https://repositorio.ufscar.br/bitstream/ufscar/14524/8/carta_comprovante.pdf.txt68b329da9893e34099c7d8ad5cb9c940MD58THUMBNAILSauloPedro.pdf.jpgSauloPedro.pdf.jpgIM Thumbnailimage/jpeg10698https://repositorio.ufscar.br/bitstream/ufscar/14524/7/SauloPedro.pdf.jpgf2df49c14111005423b98ca616f4abd1MD57carta_comprovante.pdf.jpgcarta_comprovante.pdf.jpgIM Thumbnailimage/jpeg11224https://repositorio.ufscar.br/bitstream/ufscar/14524/9/carta_comprovante.pdf.jpgeaf2240f5e5c1660ebf1da9d65bf3604MD59ufscar/145242023-09-18 18:32:12.603oai:repositorio.ufscar.br:ufscar/14524Repositório InstitucionalPUBhttps://repositorio.ufscar.br/oai/requestopendoar:43222023-09-18T18:32:12Repositório Institucional da UFSCAR - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.eng.fl_str_mv |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
dc.title.alternative.por.fl_str_mv |
Conversing learning: aplicando o conhecimento coletivo para auxiliar tarefas de aprendizado sem fim |
title |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
spellingShingle |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks Pedro, Saulo Domingos de Souza Never ending learning Conversing learning Social Web CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
title_short |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
title_full |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
title_fullStr |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
title_full_unstemmed |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
title_sort |
Conversing learning: applying the wisdom of crowds to assist never ending learning tasks |
author |
Pedro, Saulo Domingos de Souza |
author_facet |
Pedro, Saulo Domingos de Souza |
author_role |
author |
dc.contributor.authorlattes.por.fl_str_mv |
http://lattes.cnpq.br/4984413906818753 |
dc.contributor.author.fl_str_mv |
Pedro, Saulo Domingos de Souza |
dc.contributor.advisor1.fl_str_mv |
Hruschka Júnior, Estevam Rafael |
dc.contributor.advisor1Lattes.fl_str_mv |
http://lattes.cnpq.br/2097340857065853 |
dc.contributor.authorID.fl_str_mv |
1a76caac-d109-4eed-bf1a-0761a1b1fb54 |
contributor_str_mv |
Hruschka Júnior, Estevam Rafael |
dc.subject.eng.fl_str_mv |
Never ending learning Conversing learning Social Web |
topic |
Never ending learning Conversing learning Social Web CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::TEORIA DA COMPUTACAO |
description |
In Machine Learning systems we often apply techniques are often applied to learn about real world problems behavior from data. Traditionally, such data comes from instances of datasets (that represents the target problem) which we want to learn from. This approach has been broadly used in many different application domains such as recommending systems, meteorological prediction, medical diagnosis, etc. The recent years of quick development of communications technology, that made the Internet faster and available, made possible the acquisition of information to feed a growing number of Machine Learning applications and, in addition, brought light to the use of human computation and crowdsourcing approaches commonly applied to problems that are easy for human but difficult for computers. Thus, the Social Web has been the focus of many research in Artificial Intelligence and Machine Learning. In this work we want to show how we can take advantage from the Social Web to add value to Machine Learning systems which can actively and autonomously ask for web users help to improve learning performance. This work proposes a model of learning called Conversing Learning that is is based on both, Active Learning and Interactive Learning, and is intended to allow machines to convert its knowledge base into human understandable content and then, actively and autonomously ask people (Web users) to take part into the knowledge acquisition (and labeling) process. The work presents how to apply Conversing Learning tasks to assist Machine Learning tasks, and discusses the success of experiments exploring the subtleties of this model. |
publishDate |
2013 |
dc.date.issued.fl_str_mv |
2013-09-21 |
dc.date.accessioned.fl_str_mv |
2021-07-06T11:47:01Z |
dc.date.available.fl_str_mv |
2021-07-06T11:47:01Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
PEDRO, Saulo Domingos de Souza. Conversing learning: applying the wisdom of crowds to assist never ending learning tasks. 2013. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2013. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14524. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufscar.br/handle/ufscar/14524 |
identifier_str_mv |
PEDRO, Saulo Domingos de Souza. Conversing learning: applying the wisdom of crowds to assist never ending learning tasks. 2013. Dissertação (Mestrado em Ciência da Computação) – Universidade Federal de São Carlos, São Carlos, 2013. Disponível em: https://repositorio.ufscar.br/handle/ufscar/14524. |
url |
https://repositorio.ufscar.br/handle/ufscar/14524 |
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eng |
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eng |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
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openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de São Carlos Câmpus São Carlos |
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Programa de Pós-Graduação em Ciência da Computação - PPGCC |
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UFSCar |
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Universidade Federal de São Carlos Câmpus São Carlos |
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