Relevant bibliographies by topics / Never ending learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Never ending learning'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Never ending learning"

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Mitchell, T., W. Cohen, E. Hruschka, et al. "Never-ending learning." Communications of the ACM 61, no. 5 (2018): 103–15. http://dx.doi.org/10.1145/3191513.

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Mottrie, Alexander, and Giacomo Novara. "Is surgery a never-ending learning process?" BJU International 114, no. 4 (2014): 472–73. http://dx.doi.org/10.1111/bju.12694.

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Hui, Kenneth C. W., Feng Zhang, William W. Shaw, et al. "Learning curve of microvascular venous anastomosis: A never ending struggle?" Microsurgery 20, no. 1 (2000): 22–24. http://dx.doi.org/10.1002/(sici)1098-2752(2000)20:1<22::aid-micr4>3.0.co;2-m.

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Morrow, Mary R. "Student Plagiarism: Never-Ending Challenges and Possibilities for Faculty." Nursing Science Quarterly 34, no. 4 (2021): 372–73. http://dx.doi.org/10.1177/08943184211031592.

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Student plagiarism is a never-ending challenge for faculty. This column introduction shares faculty experiences as well as some successful interventions. The author grounds faculty student plagiarism struggles with the humanbecoming teaching-learning module and reminds faculty to address the issue for the benefit of the student and the discipline.
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Henscheid, Jean M. "From the editor: The never-ending task of understanding student learning." About Campus 14, no. 3 (2009): 1. http://dx.doi.org/10.1002/abc.288.

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Chen, Yanping, Yuan Hao, Thanawin Rakthanmanon, Jesin Zakaria, Bing Hu, and Eamonn Keogh. "A general framework for never-ending learning from time series streams." Data Mining and Knowledge Discovery 29, no. 6 (2014): 1622–64. http://dx.doi.org/10.1007/s10618-014-0388-4.

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Hruschka Jr., E. R., M. C. Duarte, and M. C. Nicoletti. "Coupling as Strategy for Reducing Concept-Drift in Never-ending Learning Environments." Fundamenta Informaticae 124, no. 1-2 (2013): 47–61. http://dx.doi.org/10.3233/fi-2013-824.

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Aleandri, Gabriella, and Luca Girotti. "Lifelong Learning and Training:A Never Ending Challenge and Choice for Educational System." Procedia - Social and Behavioral Sciences 46 (2012): 1406–12. http://dx.doi.org/10.1016/j.sbspro.2012.05.311.

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Gates, Bob. "Value of learning disabled people and the never ending appeal of eugenics." Journal of Learning Disabilities for Nursing, Health, and Social Care 1, no. 4 (1997): 159–61. http://dx.doi.org/10.1177/146900479700100401.

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Cardenas Claros, Monica Stella. "Psycho-linguistic and socio-cultural approaches to language learning: A never ending debate." Colombian Applied Linguistics Journal, no. 10 (April 4, 2011): 142. http://dx.doi.org/10.14483/22487085.102.

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This paper critically examines psycholinguistic and socio-cultural approaches for language learning. It provides a thorough discussion of the ontological positions where they originate, the methods they use, some relevant work under each perspective and most importantly criticisms that each perspective faces. The paper concludes that no approach is better than the other and advocates for collaboration projects nurtured from both perspectives.
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Dissertations / Theses on the topic "Never ending learning"

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Maziero, Erick Galani. "Análise retórica com base em grande quantidade de dados." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-13012017-103446/.

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Com uma quantidade quase incontável de informação textual disponível na web, a automatização de diversas tarefas referentes ao processamento automático de textos é uma necessidade inegável. Em abordagens superficiais do PLN (Processamento da Linguagem Natural), importantes propriedades do texto são perdidas, como posição, ordem, adjacência e contexto dos segmentos textuais. Uma análise textual mais profunda, como a realizada no nível do discurso, ocupa-se da busca e identificação da organização retórica do texto, gerando uma estrutura hierárquica em que as intenções do autor são explicitadas e relacionadas entre si. Para a automatização dessa tarefa, tem-se utilizado técnicas de aprendizado automático, predominantemente do paradigma supervisionado. Nesse paradigma, são necessários dados rotulados manualmente para a geração dos modelos de classificação. Como a anotação para essa tarefa é algo custoso, os resultados obtidos no aprendizado são insatisfatórios, pois estão bem aquém do desempenho humano na mesma tarefa. Nesta tese, o uso massivo de dados não rotulados no aprendizado semissupervisionado sem fim foi empregado na tarefa de identificação das relações retóricas. Foi proposto um framework que utiliza textos obtidos continuamente da web. No framework, realiza-se a monitoração da mudança de conceito, que pode ocorrer durante o aprendizado contínuo, e emprega-se uma variação dos algoritmos tradicionais de semissupervisão. Além disso, foram adaptados para o Português técnicas do estado da arte. Sem a necessidade de anotação humana, a medida-F melhorou, por enquanto, em 0,144 (de 0,543 para 0,621). Esse resultado consiste no estado da arte da análise discursiva automática para o Português.<br>Considering the almost uncountable textual information available on the web, the auto- matization of several tasks related to the automatic text processing is an undeniable need. In superficial approaches of NLP (Natural Language Processing), important properties of the text are lost, as position, order, adjacency and context of textual segments. A de- eper analysis, as carried out in the discursive level, deals with the identification of the rhetoric organization of the text, generating a hierarchical structure. In this structure, the intentions of the author are identified and related among them. To the automati- zation of this task, most of the works have used machine learning techniques, mainly from the supervised paradigm. In this paradigm, manually labeled data is required to obtain classification models, specially to identify the rhetorical relations. As the manual annotation is a costly process, the obtained results in the task are unsatisfactory, because they are below the human perfomance. In this thesis, the massive use of unlabeled data was applied in a semi-supervised never-ending learning to identify the rhetorical relations. In this exploration, a framework was proposed, which uses texts continuously obtained from the web. In the framework, a variation of traditional semi-supervised algorithms was employed, and it uses a concept-drift monitoring strategy. Besides that, state of the art techniques for English were adapted to Portuguese. Without the human intervention, the F-measure increased, for while, 0.144 (from 0.543 to 0.621). This result consists in the state-of-the-art for Discourse Analysis in Portuguese.
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Gotardo, Reginaldo Aparecido. "Uma abordagem de sistema de recomendação orientada pelo aprendizado sem fim." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/292.

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Made available in DSpace on 2016-06-02T19:03:59Z (GMT). No. of bitstreams: 1 6340.pdf: 3337556 bytes, checksum: 693a6a9cfb4dc2a26651724099fcf890 (MD5) Previous issue date: 2014-02-28<br>Financiadora de Estudos e Projetos<br>Recommender Systems have a very well defined function: recommend something to someone. Through Artificial Intelligence techniques, more particularly from areas such as Data Mining and Machine Learning, it is possible to build recommendation systems. These systems will analyze large amounts of data and will inform users about some items that will probably interest them. However, some limitations of the recommender systems, which are sometimes, caused by the Mining or Learning models themselves or by the lack of available data make them computationally expensive or inaccurate. Besides, recommender systems in real environments are dynamic: data change over time or with new ratings, new users, new items or when user updates previous ratings. The Never Ending-Learning Approach (NEL) aims at a self-supervised and self-reflexive learning to mainly maximize learning of a system based on data from several sources, algorithms that can cooperate to make a better knowledge base considering the dynamic of real learning problems: learning improves along the time. As mentioned before, recommender systems are dynamic and depend on data between user and items. In order to minimize this dependency and to provide meaningful and useful results to users, this work presents a Recommender System approach guided by NEL Principles. Results show that it is possible to minimize or delay the data dependency through classifiers coupling techniques and concept deviation control. Due to that, it is possible to start with little data from a recommender system that will be dynamic and will receive new information. These new information will help even more in controlling the concept deviation and promoting the most useful recommendations. Then, this thesis presents how the Recommender System guided by NEL principles can contribute to the state of the art in recommender systems and implement a system with practical results through the Never-Ending Learning Approach.<br>Os Sistemas de Recomendação possuem uma função muito bem definida: recomendar algo a alguém. Através de técnicas de Inteligência Artificial, mais particularmente de áreas como a Mineração de Dados e o Aprendizado de Máquina é possível construir Sistemas de Recomendação que analisem grandes volumes de dados e consigam predizer aos usuários algo que provavelmente irá lhes interessar. No entanto, algumas limitações dos Sistemas de Recomendações, causadas as vezes pelos próprios modelos de Mineração ou Aprendizado utilizados ou pela escassez dos dados disponíveis, os tornam computacionalmente caros ou imprecisos. Além disto, Sistemas de Recomendação em ambientes reais são dinâmicos, ou seja, os dados mudam com o passar do tempo seja com novas avaliações, novos usuários, novos itens ou mesmo atualizações de avaliações anteriores. A abordagem de Aprendizado Sem-Fim (SASF) visa um aprendizado autossupervisionado e autorreflexivo para, sobretudo, maximizar o aprendizado de um sistema com base em dados de fontes diversas, algoritmos que cooperem entre si para melhor modelar uma base de conhecimento e considerar a dinamicidade de problemas reais de aprendizado: Aprender amadurece com o tempo. Como já dito, sistemas de recomendação são dinâmicos e dependem de dados entre usuários e itens. Para minimizar esta dependência e prover resultados significativos e úteis aos usuários é apresentada neste trabalho uma abordagem de Sistema de Recomendação orientada pelos Princípios do Aprendizado Sem-Fim. Os resultados obtidos sugerem que é possível minimizar ou retardar a dependência de dados através de técnicas de acoplamento de classificadores e do controle do desvio de conceito. Com isto, é possível atuar com poucos dados de um sistema de recomendação que será dinâmico e receberá novas informações. Estas novas informações auxiliarão ainda mais no controle do desvio de conceito e na promoção de recomendações mais úteis. Por tudo isto, este trabalho apresenta como proposta o desenvolvimento de uma Abordagem para Sistemas de Recomendação baseada no Aprendizado Sem Fim, como forma de contribuir para o estado da arte em sistemas de recomendação e de implementar um sistema com resultados práticos através do Aprendizado sem Fim.
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Duarte, Maísa Cristina. "Leitura da web em português em ambiente de aprendizado sem-fim." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/8414.

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Submitted by Alison Vanceto (alison-vanceto@hotmail.com) on 2017-01-03T12:49:19Z No. of bitstreams: 1 TeseMCD.pdf: 1564245 bytes, checksum: fbb9eb1099a1b38351371c97e8e49bb4 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2017-01-16T16:47:27Z (GMT) No. of bitstreams: 1 TeseMCD.pdf: 1564245 bytes, checksum: fbb9eb1099a1b38351371c97e8e49bb4 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2017-01-16T16:47:38Z (GMT) No. of bitstreams: 1 TeseMCD.pdf: 1564245 bytes, checksum: fbb9eb1099a1b38351371c97e8e49bb4 (MD5)<br>Made available in DSpace on 2017-01-16T16:47:46Z (GMT). No. of bitstreams: 1 TeseMCD.pdf: 1564245 bytes, checksum: fbb9eb1099a1b38351371c97e8e49bb4 (MD5) Previous issue date: 2016-01-04<br>Não recebi financiamento<br>NELL is a computer system that has the goal of learn to learn 24 hours per day, continuously and learn more an better than the last day, to perform the knowledge base (KB). NELL is running since January 12 of 2010. Furthermore, NELL goals is have hight precision to be able to continue the learning. NELL is developed in macro-reading context, because this NELL needs very much redundancy to run. The first step to run NELL is to have an big (all-pairs-data). An all-pairs-data is a preprocessed base using Natural Language Processing (NLP), that base has all sufficient statistics about a corpus of web pages. The proposal of this project was to create a instance of NELL (currently in English) in Portuguese. For this, the first goal was the developing an all-pairs-data in Portuguese. The second step was to create a new version of Portuguese NELL. And finally, the third goal was to develop a coreference resolution hybrid method focused in features semantics and morphologics. This method is not dependent of a specific language, it is can be applied for another languages with the same alphabet of Portuguese language. The NELL in Portuguese was developed, but the all-pairs-data is not big enough. Because it Portuguese NELL is not running for ever, like the English version. Even so, this project present the steps about how to develop a NELL in other language and some ideas about how to improve the all-pairs-data. By the way, this project present a coreference resolution hybrid method with good results to NELL.<br>A NELL é um sistema de computador que possui o objetivo de executar 24 horas por dia, 7 dias por semana, sem parar. A versão atual da NELL foi iniciada em 12 de Janeiro de 2010 e continua ativa. Seu objetivo é aprender cada vez mais fatos da web para popular sua base de conhecimento (Knowlegde Base - KB). Além de aprender cada vez mais, a NELL também objetiva alcançar alta confiança no aprendizado para garantir a continuidade do aprendizado. A NELL foi desenvolvida e atua no contexto da macroleitura, no qual é necessária uma grande quantidade e redundância de dados. Para que o sistema possa aprender, o primeiro passo é criar uma base preprocessada (all-pairs-data) a partir do uso de técnicas linguísticas. O all-pairs-data deve possuir todas as estatísticas suficientes para a execução da NELL e também deve ser de um tamanho suficientemente grande para que o aprendizado possa ocorrer. Neste projeto, foi proposta a criação de uma nova instância da NELL em português. Inicialmente foi proposta a criação de um all-pairs-data e, em seguida, a criação de uma abordagem híbrida para a resolução de correferências independente de língua por base em características semânticas e morfológicas. A proposta híbrida objetivou aperfeiçoar o processo atual de tratamento de correferências na NELL, melhorando assim a confiabilidade no aprendizado. Todas as propostas foram desenvolvidas e a NELL em português obteve bons resultados. Tais resultados evidenciam que a leitura da web em português poderá se tornar um sistema de aprendizado sem-fim. Para que isso ocorra são também apresentadas as futuras abordagens e propostas. Além disso, este projeto apresenta a metodologia de criação da instância da NELL em português, uma proposta de resolução de correferência que explora atributos linguisticos,bem como a ontologia da NELL, além de apontar trabalhos futuros, nos quais inclui-se processos de adição de outras línguas na NELL, principalmente para aquelas que possuem poucas páginas web disponíveis para o aprendizado.
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Polastri, Paulo César. "Aprendizado sem-fim de paráfrases." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7868.

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Submitted by Luciana Sebin (lusebin@ufscar.br) on 2016-10-05T18:38:23Z No. of bitstreams: 1 DissPCP.pdf: 1921482 bytes, checksum: 5298cc1a066e0cfe217b2b9c61076e65 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:13:08Z (GMT) No. of bitstreams: 1 DissPCP.pdf: 1921482 bytes, checksum: 5298cc1a066e0cfe217b2b9c61076e65 (MD5)<br>Approved for entry into archive by Marina Freitas (marinapf@ufscar.br) on 2016-10-14T14:13:18Z (GMT) No. of bitstreams: 1 DissPCP.pdf: 1921482 bytes, checksum: 5298cc1a066e0cfe217b2b9c61076e65 (MD5)<br>Made available in DSpace on 2016-10-14T14:13:28Z (GMT). No. of bitstreams: 1 DissPCP.pdf: 1921482 bytes, checksum: 5298cc1a066e0cfe217b2b9c61076e65 (MD5) Previous issue date: 2016-03-04<br>Não recebi financiamento<br>Use different words to express/convey the same message is a necessity in any natural language and, as such, should be investigated in research in Natural Language Processing (NLP). When it is just a simple word, we say that the interchangeable words are synonyms; while the term paraphrase is used to express a more general idea and that also may involve more than one word. For example, the sentences "the light is red" and "the light is closed" are examples of paraphrases as "sign" and "traffic light" represent synonymous in this context. Proper treatment of paraphrasing is important in several NLP applications, such as Machine Translation, which paraphrases can be used to increase the coverage of Statistical Machine Translation systems; on Multidocument Summarization, where paraphrases identification allows the recognition of repeated information; and Natural Language Generation, where the generation of paraphrases allows creating more varied and fluent texts. The project described in this document is intended to verify that is possible to learn, in an incremental and automatic way, paraphrases in words level from a bilingual parallel corpus, using Never-Ending Machine Learning (NEML) strategy and the Internet as a source of knowledge. The NEML is a machine learning strategy, based on how humans learn: what is learned previously can be used to learn new information and perhaps more complex in the future. Thus, the NEML has been applied together with the strategy for paraphrases extraction proposed by Bannard and Callison-Burch (2005) where, from bilingual parallel corpus, paraphrases are extracted using a pivot language. In this context, it was developed NEPaL (Never-Ending Paraphrase Learner) AMSF system responsible for: (1) extract the internet texts, (2) align the text using a pivot language, (3) rank the candidates according to a classification model and (4) use the knowledge to produce a new classifier model and therefore gain more knowledge restarting the never-ending learning cycle.<br>Usar palavras diferentes para expressar/transmitir a mesma mensagem é uma necessidade em qualquer língua natural e, como tal, deve ser investigada nas pesquisas em Processamento de Língua Natural (PLN). Quando se trata apenas de uma palavra simples, dizemos que as palavras intercambiáveis são sinônimos; enquanto o termo paráfrase é utilizado para expressar uma ideia mais geral e que pode envolver também mais de uma palavra. Por exemplo, as sentenças “o sinal está vermelho” e “o semáforo está fechado” são exemplo de paráfrases enquanto “sinal” e “semáforo” representam sinônimos, nesse contexto. O tratamento adequado de paráfrases é importante em diversas aplicações de PLN, como na Tradução Automática, onde paráfrases podem ser utilizadas para aumentar a cobertura de sistemas de Tradução Automática Estatística; na Sumarização Multidocumento, onde a identificação de paráfrases permite o reconhecimento de informações repetidas; e na Geração de Língua Natural, onde a geração de paráfrases permite criar textos mais variados e fluentes. O projeto descrito neste documento visa verificar se é possível aprender, de modo incremental e automático, paráfrases em nível de palavras a partir de corpus paralelo bilíngue, utilizando a estratégia de Aprendizado de Máquina Sem-fim (AMSF) e a Internet como fonte de conhecimento. O AMSF é uma estratégia de Aprendizado de Máquina, baseada na forma como os humanos aprendem: o que é aprendido previamente pode ser utilizado para aprender informações novas e talvez mais complexas, futuramente. Para tanto, o AMSF foi aplicado juntamente com a estratégia para a extração de paráfrases proposta por Bannard e Callison-Burch (2005) onde, a partir de corpus paralelo bilíngue, paráfrases são extraídas utilizando um idioma pivô. Nesse contexto, foi desenvolvido o NEPaL (Never-Ending Paraphrase Learner), sistema de AMSF responsável por: (1) extrair textos da internet, (2) alinhar os textos utilizando um idioma pivô, (3) classificar as candidatas de acordo com um modelo de classificação e (4) utilizar o conhecimento para produzir um novo modelo classificador e, consequentemente, adquirir mais conhecimento reiniciando o ciclo de aprendizado sem-fim.
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Books on the topic "Never ending learning"

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Andrew, Sandy. Your Never-Ending Life (Universal Learning Series). Bridgeway Books, 2006.

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Book chapters on the topic "Never ending learning"

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Béroule, Dominique. "The Never-Ending Learning." In Neural Computers. Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-83740-1_24.

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Vandenberghe, Roland. "School Reform-A Never-Ending Story." In The Wiley Handbook of Teaching and Learning. John Wiley & Sons, Inc., 2018. http://dx.doi.org/10.1002/9781118955901.ch2.

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Oliverio, Vinicius, and Estevam R. Hruschka. "Contradiction Detection and Ontology Extension in a Never-Ending Learning System." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34654-5_1.

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Pedro, Saulo D. S., and Estevam R. Hruschka. "Conversing Learning: Active Learning and Active Social Interaction for Human Supervision in Never-Ending Learning Systems." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34654-5_24.

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dos Santos, Edimilson Batista, Massilon Lourenço Fernandes, Estevam R. Hruschka, and Maísa Cristina Duarte. "Bayesian Networks for Identifying Semantic Relations in a Never-Ending Learning System." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53480-0_28.

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Turkanis, Carolyn Goodman, and Leslee Bartlett. "Never-Ending Learning." In Learning Together. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195097535.003.0037.

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In the OC, teachers, parents, and administrators continually try to articulate the overarching principles of learning as a community that guide our everyday practices and underlie natural variations across individuals and classrooms. The principles are enacted in varying ways in the specific practices across classrooms, as variations on the theme that makes up the common thread of the philosophy. The everyday practices that support the philosophy vary according to teacher style and experience, classroom grade levels, and the unique interests and needs of members of the classroom community. For example, some years different teachers experiment with the schedules in their classrooms, arranging all literacy activities in one time block or encouraging varying types of activities within any time block. But such variations in everyday practices are still built around the philosophical principle of purposeful learning activities, as all classrooms support literacy learning with classic and current children's literature that is of interest in their class. The specific types of activities vary across the grade levels to adapt to the interests and growing skills of the students. Teachers and parents continually examine how everyday practices in the different classrooms fit with the OC philosophy. The common principles that tie our classes together not only provide coherence to the way we do things but also underlie many of the issues with which we continue to struggle in philosophical discussions. That is natural, since a community of learners is a work in progress. The common philosophy has developed from and is understood by working together and having innumerable discussions about the way we do things. The variations in practices still must remain true to the core principles in order for the school to remain a coherent community of learners. On occasion, differences in interpretation of the philosophy by particular individuals have been great enough to raise concern. At such times, the teachers observe and reflect to come to consensus on how to support the learning of the people involved. With this process, people usually come to understand and embrace the philosophy; sometimes they realize that another learning situation would be more appropriate for them.
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"A NEVER-ENDING WAR?" In Learning to Forget. Stanford University Press, 2013. http://dx.doi.org/10.2307/j.ctvqsf1s4.12.

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Fitzgerald, David. "A NEVER-ENDING WAR?" In Learning to Forget. Stanford University Press, 2013. http://dx.doi.org/10.11126/stanford/9780804785815.003.0008.

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"8 A Never-Ending War?" In Learning to Forget. Stanford University Press, 2020. http://dx.doi.org/10.1515/9780804786423-010.

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Ross, David Brian, and Gina Lynne Peyton. "The Never Ending Intellectual Theft of Truth." In Deep Fakes, Fake News, and Misinformation in Online Teaching and Learning Technologies. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-6474-5.ch003.

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The purpose of this chapter is to examine how the fake news has originated. This term has been in existence for decades, since the evolution of the printing press, which also disseminated false information. The mainstream media and non-mainstream media or just individuals in general have their own biases and agendas, so misinformation, disinformation, exaggerations, and deceptions will exist. This chapter will provide individuals from any political perspective or other beliefs evidence to make their own judgements. Digital citizenship and literacy will be explored using various examples of obtaining information and use of devices. In addition, this chapter will consider how researchers should take risks to explore controversial topics such as fake news to inform an audience using research.
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Conference papers on the topic "Never ending learning"

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Mitchell, Tom, and E. Fredkin. "Never-ending language learning." In 2014 IEEE International Conference on Big Data (Big Data). IEEE, 2014. http://dx.doi.org/10.1109/bigdata.2014.7004203.

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Rondon, Alexandre, Helena Caseli, and Carlos Ramisch. "Never-Ending Multiword Expressions Learning." In Proceedings of the 11th Workshop on Multiword Expressions. Association for Computational Linguistics, 2015. http://dx.doi.org/10.3115/v1/w15-0908.

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Dragoni, Aldo Franco, Germano Vallesi, and Paola Baldassarri. "Hybrid system for a never-ending unsupervised learning." In 2010 10th International Conference on Hybrid Intelligent Systems (HIS 2010). IEEE, 2010. http://dx.doi.org/10.1109/his.2010.5601070.

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Yuyin Sun and Dieter Fox. "NEOL: Toward Never-Ending Object Learning for robots." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487302.

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Hruschka, Estevam. "Data-Driven Never-Ending Learning Question Answering Systems." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. ACM, 2020. http://dx.doi.org/10.1145/3394486.3406486.

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Hao, Yuan, Yanping Chen, Jesin Zakaria, Bing Hu, Thanawin Rakthanmanon, and Eamonn Keogh. "Towards never-ending learning from time series streams." In KDD' 13: The 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2013. http://dx.doi.org/10.1145/2487575.2487634.

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Markov, Konstantin, and Satoshi Nakamura. "Never-ending learning with dynamic hidden Markov network." In Interspeech 2007. ISCA, 2007. http://dx.doi.org/10.21437/interspeech.2007-418.

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Markov, Konstantin, and Satoshi Nakamura. "Never-ending learning system for on-line speaker diarization." In 2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU). IEEE, 2007. http://dx.doi.org/10.1109/asru.2007.4430197.

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Buraya, Kseniya, Lidia Pivovarova, Sergey Budkov, and Andrey Filchenkov. "Towards Never Ending Language Learning for Morphologically Rich Languages." In Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/w17-1417.

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Abujabal, Abdalghani, Rishiraj Saha Roy, Mohamed Yahya, and Gerhard Weikum. "Never-Ending Learning for Open-Domain Question Answering over Knowledge Bases." In the 2018 World Wide Web Conference. ACM Press, 2018. http://dx.doi.org/10.1145/3178876.3186004.

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