Literatura académica sobre el tema "Cross-learning"
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Artículos de revistas sobre el tema "Cross-learning"
Abarghooei, Majid. "Designing a Cross-Platform Mobile Learning System". Lecture Notes on Software Engineering 3, n.º 3 (2015): 195–98. http://dx.doi.org/10.7763/lnse.2015.v3.189.
Texto completoChok, S. "Cross organisational learning". BMJ 322, n.º 7293 (28 de abril de 2001): 2. http://dx.doi.org/10.1136/bmj.322.7293.s2-7293.
Texto completoNewell, Sue. "Enhancing Cross-Project Learning". Engineering Management Journal 16, n.º 1 (marzo de 2004): 12–20. http://dx.doi.org/10.1080/10429247.2004.11415234.
Texto completoPetersen, Maya L., Annette M. Molinaro, Sandra E. Sinisi y Mark J. van der Laan. "Cross-validated bagged learning". Journal of Multivariate Analysis 98, n.º 9 (octubre de 2007): 1693–704. http://dx.doi.org/10.1016/j.jmva.2007.07.004.
Texto completoNayan, Surina, Hariharan N. Krishnasamy y Latisha Asmaak Shafie. "A Cross-National Study of Motivation in Language Learning". International Journal of Information and Education Technology 4, n.º 2 (2014): 194–97. http://dx.doi.org/10.7763/ijiet.2014.v4.397.
Texto completoNie, Weizhi, Anan Liu, Wenhui Li y Yuting Su. "Cross-view action recognition by cross-domain learning". Image and Vision Computing 55 (noviembre de 2016): 109–18. http://dx.doi.org/10.1016/j.imavis.2016.04.011.
Texto completoEliawati, Titim. "CROSS CULTURAL UNDERSTANDING LEARNING METHOD". Journal MELT (Medium for English Language Teaching) 3, n.º 1 (29 de enero de 2019): 17. http://dx.doi.org/10.22303/melt.3.1.2018.14-26.
Texto completoHan, Pi-Chi y John A. Henschke. "Cross-Cultural Learning and Mentoring". International Journal of Adult Vocational Education and Technology 3, n.º 3 (julio de 2012): 26–36. http://dx.doi.org/10.4018/javet.2012070103.
Texto completoBonometti, Stefano. "Learning in Cross-Media Environment". International Journal of Web-Based Learning and Teaching Technologies 12, n.º 4 (octubre de 2017): 48–57. http://dx.doi.org/10.4018/ijwltt.2017100105.
Texto completoMiller, Anne. "Design for cross-cultural learning". International Journal of Intercultural Relations 12, n.º 3 (enero de 1988): 296–97. http://dx.doi.org/10.1016/0147-1767(88)90022-3.
Texto completoTesis sobre el tema "Cross-learning"
Zhang, Li. "Cross-view learning". Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43185.
Texto completoSi, Si y 斯思. "Cross-domain subspace learning". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44912912.
Texto completoHjelm, Hans. "Cross-language Ontology Learning : Incorporating and Exploiting Cross-language Data in the Ontology Learning Process". Doctoral thesis, Stockholms universitet, Institutionen för lingvistik, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-8414.
Texto completoFör att köpa boken skicka en beställning till exp@ling.su.se/ To order the book send an e-mail to exp@ling.su.se
Zhu, Xiaodan. "On Cross-Series Machine Learning Models". W&M ScholarWorks, 2020. https://scholarworks.wm.edu/etd/1616444550.
Texto completoFohlin, Robert. "A cross-media game environment for learning". Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-9314.
Texto completoKodirov, Elyor. "Cross-class transfer learning for visual data". Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.
Texto completoPorto, Faimison Rodrigues. "Cross-project defect prediction with meta-Learning". Universidade de São Paulo, 2017. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-21032018-163840/.
Texto completoModelos de predição de defeitos auxiliam profissionais de teste na priorização de partes do software mais propensas a conter defeitos. A abordagem de predição de defeitos cruzada entre projetos (CPDP) refere-se à utilização de projetos externos já conhecidos para compor o conjunto de treinamento. Essa abordagem é útil quando a quantidade de dados históricos de defeitos é inapropriada ou insuficiente para compor o conjunto de treinamento. Embora o princípio seja atrativo, o desempenho de predição é um fator limitante nessa abordagem. Nos últimos anos, vários métodos foram propostos com o intuito de melhorar o desempenho de predição de modelos CPDP. Contudo, na literatura, existe uma carência de estudos comparativos que apontam quais métodos CPDP apresentam melhores desempenhos. Além disso, não há evidências sobre quais métodos CPDP apresentam melhor desempenho para um domínio de aplicação específico. De fato, não existe um algoritmo de aprendizado de máquina que seja apropriado para todos os domínios de aplicação. A tarefa de decisão sobre qual algoritmo é mais adequado a um determinado domínio de aplicação é investigado na literatura de meta-aprendizado. Um modelo de meta-aprendizado é caracterizado pela sua capacidade de aprender a partir de experiências anteriores e adaptar seu viés de indução dinamicamente de acordo com o domínio alvo. Neste trabalho, nós investigamos a viabilidade de usar meta-aprendizado para a recomendação de métodos CPDP. Nesta tese são almejados três principais objetivos. Primeiro, é conduzida uma análise experimental para investigar a viabilidade de usar métodos de seleção de atributos como procedimento interno de dois métodos CPDP, com o intuito de melhorar o desempenho de predição. Segundo, são investigados quais métodos CPDP apresentam um melhor desempenho em um contexto geral. Nesse contexto, também é investigado se os métodos com melhor desempenho geral apresentam melhor desempenho para os mesmos conjuntos de dados (ou projetos de software). Os resultados revelam que os métodos CPDP mais adequados para um projeto podem variar de acordo com as características do projeto sendo predito. Essa constatação conduz à terceira investigação realizada neste trabalho. Foram investigadas as várias particularidades inerentes ao contexto CPDP a fim de propor uma solução de meta-aprendizado capaz de aprender com experiências anteriores e recomendar métodos CPDP adequados, de acordo com as características do software. Foram avaliados a capacidade de meta-aprendizado da solução proposta e a sua performance em relação aos métodos base que apresentaram melhor desempenho geral.
Ciucanu, Radu. "Cross-model queries and schemas : complexity and learning". Thesis, Lille 1, 2015. http://www.theses.fr/2015LIL10056/document.
Texto completoSpecifying a database query using a formal query language is typically a challenging task for non-expert users. In the context of big data, this problem becomes even harder because it requires the users to deal with database instances of large size and hence difficult to visualize. Such instances usually lack a schema to help the users specify their queries, or have an incomplete schema as they come from disparate data sources. In this thesis, we address the problem of query specification for non-expert users. We identify two possible approaches for tackling this problem: learning queries from examples and translating the data in a format that the user finds easier to query. Our contributions are aligned with these two complementary directions and span over three of the most popular data models: XML, relational, and graph. This thesis consists of two parts, dedicated to (i) schema definition and translation, and to (ii) learning schemas and queries. In the first part, we define schema formalisms for unordered XML and we analyze their computational properties; we also study the complexity of the data exchange problem in the setting of a relational source and a graph target database. In the second part, we investigate the problem of learning from examples the schemas for unordered XML proposed in the first part, as well as relational join queries and path queries on graph databases. The interactive scenario that we propose for these two classes of queries is immediately applicable to assisting non-expert users in the process of query specification
Weatherholtz, Kodi. "Perceptual learning of systemic cross-category vowel variation". The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1429782580.
Texto completoNerantzi, Chrissi. "Towards a framework for cross-boundary collaborative open learning for cross-institutional academic development". Thesis, Edinburgh Napier University, 2017. http://researchrepository.napier.ac.uk/Output/1025583.
Texto completoLibros sobre el tema "Cross-learning"
Korhonen, Vesa. Cross-cultural lifelong learning. Tampere: Tampere University Press, 2010.
Buscar texto completoStephen, Bochner, Brislin Richard W. 1945-, Lonner Walter J y East-West Culture Learning Institute, eds. Cross-cultural perspectives on learning. Ann Arbor, Mich: University Microfilms International, 1987.
Buscar texto completoSikkema, Mildred. Design for cross-cultural learning. Yarmouth, Me: Intercultural Press, 1987.
Buscar texto completo1937-, Berendt Erich Adalbert, ed. Metaphors for learning: Cross-cultural perspectives. Amsterdam: John Benjamins Publishing, 2008.
Buscar texto completoWestwood, Peter S. Teaching and learning difficulties: Cross-curricular perspectives. Camberwell, Vic: ACER Press, 2006.
Buscar texto completoZhu, Sijia Cynthia, Shu Xie, Yunpeng Ma y Douglas McDougall, eds. Reciprocal Learning for Cross-Cultural Mathematics Education. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56838-2.
Texto completoDavis, Sarah H. Being there: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.
Buscar texto completoRingbom, Håkan. Cross-linguistic similarity in foreign language learning. Clevedon [England]: Multilingual Matters, 2007.
Buscar texto completoDavis, Sarah H. Resident aliens: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.
Buscar texto completoYihong, Fan, ed. Assuring university learning quality: Cross-boundary collaboration. Trondheim: Tapir Academic Press, 2006.
Buscar texto completoCapítulos de libros sobre el tema "Cross-learning"
Delaney, Laurel J. "Cross-Cultural Learning". En Exporting, 413–22. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5792-9_24.
Texto completoSkocaj, Danijel, Ales Leonardis y Geert-Jan M. Kruijff. "Cross-Modal Learning". En Encyclopedia of the Sciences of Learning, 861–64. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_239.
Texto completoDelaney, Laurel J. "Cross-Cultural Learning". En Exporting, 451–61. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2193-8_24.
Texto completoSmith, Andrew D. M. y Kenny Smith. "Cross-Situational Learning". En Encyclopedia of the Sciences of Learning, 864–66. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_1712.
Texto completoHibbert, Liesel y Gregory Kerr. "Cross-disciplinary learning". En English as a Language of Learning, Teaching and Inclusivity, 143–54. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003382645-9.
Texto completoSchaffer, Scott P. "Cross-Disciplinary Team Learning". En Handbook of Improving Performance in the Workplace: Selecting and Implementing Performance Interventions, 598–612. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470587102.ch25.
Texto completoSchaffer, Scott P. "Cross-Disciplinary Team Learning". En Handbook of Improving Performance in the Workplace: Volumes 1-3, 598–612. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2010. http://dx.doi.org/10.1002/9780470592663.ch44.
Texto completoApfelthaler, Gerhard. "Cross-Cultural Learning Styles". En Encyclopedia of the Sciences of Learning, 853–55. Boston, MA: Springer US, 2012. http://dx.doi.org/10.1007/978-1-4419-1428-6_455.
Texto completoLaFever, Marcella. "Cross-Cultural Learning Styles". En Encyclopedia of Cross-Cultural School Psychology, 286–87. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-0-387-71799-9_102.
Texto completoYao, Yuan, Zhiyuan Liu, Yankai Lin y Maosong Sun. "Cross-Modal Representation Learning". En Representation Learning for Natural Language Processing, 211–40. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1600-9_7.
Texto completoActas de conferencias sobre el tema "Cross-learning"
Fisch, Shalom M., Richard Lesh, Beth Motoki, Sandra Crespo y Vincent Melfi. "Cross-platform learning". En the 10th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1999030.1999036.
Texto completoFu, Eugene Yujun, Michael Xuelin Huang, Hong Va Leong y Grace Ngai. "Cross-Species Learning". En MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240710.
Texto completoKang, Cuicui, Shengcai Liao, Yonghao He, Jian Wang, Wenjia Niu, Shiming Xiang y Chunhong Pan. "Cross-Modal Similarity Learning". En CIKM'15: 24th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2806416.2806469.
Texto completoM’hamdi, Meryem, Xiang Ren y Jonathan May. "Cross-lingual Continual Learning". En Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2023. http://dx.doi.org/10.18653/v1/2023.acl-long.217.
Texto completoCervino, Juan, Juan Andres Bazerque, Miguel Calvo-Fullana y Alejandro Ribeiro. "Multi-task Supervised Learning via Cross-learning". En 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9615939.
Texto completoWang, Yabing, Jianfeng Dong, Tianxiang Liang, Minsong Zhang, Rui Cai y Xun Wang. "Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning". En MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548003.
Texto completoJohnson, Andrew, Penny Karanasou, Judith Gaspers y Dietrich Klakow. "Cross-lingual Transfer Learning for". En Proceedings of the 2019 Conference of the North. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/n19-2023.
Texto completoRuder, Sebastian, Anders Søgaard y Ivan Vulić. "Unsupervised Cross-Lingual Representation Learning". En Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Tutorial Abstracts. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/p19-4007.
Texto completoLiu, Alexander, SouYoung Jin, Cheng-I. Lai, Andrew Rouditchenko, Aude Oliva y James Glass. "Cross-Modal Discrete Representation Learning". En Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.acl-long.215.
Texto completoMao, WeiYang y jshardrom xia. "Cross-modal representation learning based on contrast learning". En 4th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2022), editado por Mengyi (Milly) Cen y Lidan Wang. SPIE, 2022. http://dx.doi.org/10.1117/12.2640128.
Texto completoInformes sobre el tema "Cross-learning"
Klenk, Matthew y Ken Forbus. Cross Domain Analogies for Learning Domain Theories. Fort Belvoir, VA: Defense Technical Information Center, enero de 2007. http://dx.doi.org/10.21236/ada471251.
Texto completoGarcía Betegón, Mercedes, Eva Perandones Serrano y Francisco Javier Gayo Santacecilia. Cross-cutting methodologies in learning 3D modeling. Peeref, abril de 2023. http://dx.doi.org/10.54985/peeref.2304p9515916.
Texto completoMcCloskey, Michael J., Kyle J. Behymer, Elizabeth L. Papautsky y Aniko Grandjean. Measuring Learning and Development in Cross-Cultural Competence. Fort Belvoir, VA: Defense Technical Information Center, septiembre de 2012. http://dx.doi.org/10.21236/ada568555.
Texto completoFreed, Danielle. K4D Strengthening Cross-sector Learning for Education and FCAS. Institute of Development Studies, septiembre de 2022. http://dx.doi.org/10.19088/k4d.2022.159.
Texto completoWang, Zhe, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma y Xiang Que. Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration. Purdue University, octubre de 2023. http://dx.doi.org/10.5703/1288284317663.
Texto completoThrun, Sebastian y Joseph O'Sullivan. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge,. Fort Belvoir, VA: Defense Technical Information Center, noviembre de 1995. http://dx.doi.org/10.21236/ada303253.
Texto completoShevtsiv, Nikita A. y Andrii M. Striuk. Cross platform development vs native development. CEUR Workshop Proceedings, marzo de 2021. http://dx.doi.org/10.31812/123456789/4428.
Texto completoSakurauchi, Yoko. Teaching and Learning for Intercultural Sensitivity: A Cross-Cultural Examination of American Domestic Students and Japanese Exchange Students. Portland State University Library, enero de 2000. http://dx.doi.org/10.15760/etd.1642.
Texto completoChen, Yunxiang, Jie Bao, Jianqiu Zheng, Peiyuan Gao, Qizhi He, James Stegen, Brenda Ng, Xiaofeng Liu, Roman Dibiase y Chaopeng Shen. Upscaling cross-scale flow and respiration interactions at river sediment interface leveraging observation, numerical models, and machine learning. Office of Scientific and Technical Information (OSTI), abril de 2021. http://dx.doi.org/10.2172/1769792.
Texto completoFreed, Danielle. K4D Learning Journey Strengthens the Mainstreaming of Water Security. Institute of Development Studies, septiembre de 2022. http://dx.doi.org/10.19088/k4d.2022.164.
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