Auswahl der wissenschaftlichen Literatur zum Thema „Cross-learning“
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Zeitschriftenartikel zum Thema "Cross-learning"
Abarghooei, Majid. „Designing a Cross-Platform Mobile Learning System“. Lecture Notes on Software Engineering 3, Nr. 3 (2015): 195–98. http://dx.doi.org/10.7763/lnse.2015.v3.189.
Der volle Inhalt der QuelleChok, S. „Cross organisational learning“. BMJ 322, Nr. 7293 (28.04.2001): 2. http://dx.doi.org/10.1136/bmj.322.7293.s2-7293.
Der volle Inhalt der QuelleNewell, Sue. „Enhancing Cross-Project Learning“. Engineering Management Journal 16, Nr. 1 (März 2004): 12–20. http://dx.doi.org/10.1080/10429247.2004.11415234.
Der volle Inhalt der QuellePetersen, Maya L., Annette M. Molinaro, Sandra E. Sinisi und Mark J. van der Laan. „Cross-validated bagged learning“. Journal of Multivariate Analysis 98, Nr. 9 (Oktober 2007): 1693–704. http://dx.doi.org/10.1016/j.jmva.2007.07.004.
Der volle Inhalt der QuelleNayan, Surina, Hariharan N. Krishnasamy und Latisha Asmaak Shafie. „A Cross-National Study of Motivation in Language Learning“. International Journal of Information and Education Technology 4, Nr. 2 (2014): 194–97. http://dx.doi.org/10.7763/ijiet.2014.v4.397.
Der volle Inhalt der QuelleNie, Weizhi, Anan Liu, Wenhui Li und Yuting Su. „Cross-view action recognition by cross-domain learning“. Image and Vision Computing 55 (November 2016): 109–18. http://dx.doi.org/10.1016/j.imavis.2016.04.011.
Der volle Inhalt der QuelleEliawati, Titim. „CROSS CULTURAL UNDERSTANDING LEARNING METHOD“. Journal MELT (Medium for English Language Teaching) 3, Nr. 1 (29.01.2019): 17. http://dx.doi.org/10.22303/melt.3.1.2018.14-26.
Der volle Inhalt der QuelleHan, Pi-Chi, und John A. Henschke. „Cross-Cultural Learning and Mentoring“. International Journal of Adult Vocational Education and Technology 3, Nr. 3 (Juli 2012): 26–36. http://dx.doi.org/10.4018/javet.2012070103.
Der volle Inhalt der QuelleBonometti, Stefano. „Learning in Cross-Media Environment“. International Journal of Web-Based Learning and Teaching Technologies 12, Nr. 4 (Oktober 2017): 48–57. http://dx.doi.org/10.4018/ijwltt.2017100105.
Der volle Inhalt der QuelleMiller, Anne. „Design for cross-cultural learning“. International Journal of Intercultural Relations 12, Nr. 3 (Januar 1988): 296–97. http://dx.doi.org/10.1016/0147-1767(88)90022-3.
Der volle Inhalt der QuelleDissertationen zum Thema "Cross-learning"
Zhang, Li. „Cross-view learning“. Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43185.
Der volle Inhalt der QuelleSi, Si, und 斯思. „Cross-domain subspace learning“. Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44912912.
Der volle Inhalt der QuelleHjelm, 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.
Der volle Inhalt der QuelleFör 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.
Der volle Inhalt der QuelleFohlin, 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.
Der volle Inhalt der QuelleKodirov, Elyor. „Cross-class transfer learning for visual data“. Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.
Der volle Inhalt der QuellePorto, 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/.
Der volle Inhalt der QuelleModelos 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.
Der volle Inhalt der QuelleSpecifying 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.
Der volle Inhalt der QuelleNerantzi, 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.
Der volle Inhalt der QuelleBücher zum Thema "Cross-learning"
Korhonen, Vesa. Cross-cultural lifelong learning. Tampere: Tampere University Press, 2010.
Den vollen Inhalt der Quelle findenStephen, Bochner, Brislin Richard W. 1945-, Lonner Walter J und East-West Culture Learning Institute, Hrsg. Cross-cultural perspectives on learning. Ann Arbor, Mich: University Microfilms International, 1987.
Den vollen Inhalt der Quelle findenSikkema, Mildred. Design for cross-cultural learning. Yarmouth, Me: Intercultural Press, 1987.
Den vollen Inhalt der Quelle finden1937-, Berendt Erich Adalbert, Hrsg. Metaphors for learning: Cross-cultural perspectives. Amsterdam: John Benjamins Publishing, 2008.
Den vollen Inhalt der Quelle findenWestwood, Peter S. Teaching and learning difficulties: Cross-curricular perspectives. Camberwell, Vic: ACER Press, 2006.
Den vollen Inhalt der Quelle findenZhu, Sijia Cynthia, Shu Xie, Yunpeng Ma und Douglas McDougall, Hrsg. Reciprocal Learning for Cross-Cultural Mathematics Education. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56838-2.
Der volle Inhalt der QuelleDavis, Sarah H. Being there: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.
Den vollen Inhalt der Quelle findenRingbom, Håkan. Cross-linguistic similarity in foreign language learning. Clevedon [England]: Multilingual Matters, 2007.
Den vollen Inhalt der Quelle findenDavis, Sarah H. Resident aliens: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.
Den vollen Inhalt der Quelle findenYihong, Fan, Hrsg. Assuring university learning quality: Cross-boundary collaboration. Trondheim: Tapir Academic Press, 2006.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Cross-learning"
Delaney, Laurel J. „Cross-Cultural Learning“. In Exporting, 413–22. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5792-9_24.
Der volle Inhalt der QuelleSkocaj, Danijel, Ales Leonardis und Geert-Jan M. Kruijff. „Cross-Modal Learning“. In 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.
Der volle Inhalt der QuelleDelaney, Laurel J. „Cross-Cultural Learning“. In Exporting, 451–61. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2193-8_24.
Der volle Inhalt der QuelleSmith, Andrew D. M., und Kenny Smith. „Cross-Situational Learning“. In 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.
Der volle Inhalt der QuelleHibbert, Liesel, und Gregory Kerr. „Cross-disciplinary learning“. In English as a Language of Learning, Teaching and Inclusivity, 143–54. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003382645-9.
Der volle Inhalt der QuelleSchaffer, Scott P. „Cross-Disciplinary Team Learning“. In 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.
Der volle Inhalt der QuelleSchaffer, Scott P. „Cross-Disciplinary Team Learning“. In 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.
Der volle Inhalt der QuelleApfelthaler, Gerhard. „Cross-Cultural Learning Styles“. In 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.
Der volle Inhalt der QuelleLaFever, Marcella. „Cross-Cultural Learning Styles“. In 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.
Der volle Inhalt der QuelleYao, Yuan, Zhiyuan Liu, Yankai Lin und Maosong Sun. „Cross-Modal Representation Learning“. In 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.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Cross-learning"
Fisch, Shalom M., Richard Lesh, Beth Motoki, Sandra Crespo und Vincent Melfi. „Cross-platform learning“. In the 10th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1999030.1999036.
Der volle Inhalt der QuelleFu, Eugene Yujun, Michael Xuelin Huang, Hong Va Leong und Grace Ngai. „Cross-Species Learning“. In MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240710.
Der volle Inhalt der QuelleKang, Cuicui, Shengcai Liao, Yonghao He, Jian Wang, Wenjia Niu, Shiming Xiang und Chunhong Pan. „Cross-Modal Similarity Learning“. In 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.
Der volle Inhalt der QuelleM’hamdi, Meryem, Xiang Ren und Jonathan May. „Cross-lingual Continual Learning“. In 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.
Der volle Inhalt der QuelleCervino, Juan, Juan Andres Bazerque, Miguel Calvo-Fullana und Alejandro Ribeiro. „Multi-task Supervised Learning via Cross-learning“. In 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9615939.
Der volle Inhalt der QuelleWang, Yabing, Jianfeng Dong, Tianxiang Liang, Minsong Zhang, Rui Cai und Xun Wang. „Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning“. In MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548003.
Der volle Inhalt der QuelleJohnson, Andrew, Penny Karanasou, Judith Gaspers und Dietrich Klakow. „Cross-lingual Transfer Learning for“. In 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.
Der volle Inhalt der QuelleRuder, Sebastian, Anders Søgaard und Ivan Vulić. „Unsupervised Cross-Lingual Representation Learning“. In 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.
Der volle Inhalt der QuelleLiu, Alexander, SouYoung Jin, Cheng-I. Lai, Andrew Rouditchenko, Aude Oliva und James Glass. „Cross-Modal Discrete Representation Learning“. In 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.
Der volle Inhalt der QuelleMao, WeiYang, und jshardrom xia. „Cross-modal representation learning based on contrast learning“. In 4th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2022), herausgegeben von Mengyi (Milly) Cen und Lidan Wang. SPIE, 2022. http://dx.doi.org/10.1117/12.2640128.
Der volle Inhalt der QuelleBerichte der Organisationen zum Thema "Cross-learning"
Klenk, Matthew, und Ken Forbus. Cross Domain Analogies for Learning Domain Theories. Fort Belvoir, VA: Defense Technical Information Center, Januar 2007. http://dx.doi.org/10.21236/ada471251.
Der volle Inhalt der QuelleGarcía Betegón, Mercedes, Eva Perandones Serrano und Francisco Javier Gayo Santacecilia. Cross-cutting methodologies in learning 3D modeling. Peeref, April 2023. http://dx.doi.org/10.54985/peeref.2304p9515916.
Der volle Inhalt der QuelleMcCloskey, Michael J., Kyle J. Behymer, Elizabeth L. Papautsky und Aniko Grandjean. Measuring Learning and Development in Cross-Cultural Competence. Fort Belvoir, VA: Defense Technical Information Center, September 2012. http://dx.doi.org/10.21236/ada568555.
Der volle Inhalt der QuelleFreed, Danielle. K4D Strengthening Cross-sector Learning for Education and FCAS. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.159.
Der volle Inhalt der QuelleWang, Zhe, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma und Xiang Que. Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration. Purdue University, Oktober 2023. http://dx.doi.org/10.5703/1288284317663.
Der volle Inhalt der QuelleThrun, Sebastian, und Joseph O'Sullivan. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge,. Fort Belvoir, VA: Defense Technical Information Center, November 1995. http://dx.doi.org/10.21236/ada303253.
Der volle Inhalt der QuelleShevtsiv, Nikita A., und Andrii M. Striuk. Cross platform development vs native development. CEUR Workshop Proceedings, März 2021. http://dx.doi.org/10.31812/123456789/4428.
Der volle Inhalt der QuelleSakurauchi, Yoko. Teaching and Learning for Intercultural Sensitivity: A Cross-Cultural Examination of American Domestic Students and Japanese Exchange Students. Portland State University Library, Januar 2000. http://dx.doi.org/10.15760/etd.1642.
Der volle Inhalt der QuelleChen, Yunxiang, Jie Bao, Jianqiu Zheng, Peiyuan Gao, Qizhi He, James Stegen, Brenda Ng, Xiaofeng Liu, Roman Dibiase und 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), April 2021. http://dx.doi.org/10.2172/1769792.
Der volle Inhalt der QuelleFreed, Danielle. K4D Learning Journey Strengthens the Mainstreaming of Water Security. Institute of Development Studies, September 2022. http://dx.doi.org/10.19088/k4d.2022.164.
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