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Artykuły w czasopismach na temat "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.
Pełny tekst źródłaChok, S. "Cross organisational learning". BMJ 322, nr 7293 (28.04.2001): 2. http://dx.doi.org/10.1136/bmj.322.7293.s2-7293.
Pełny tekst źródłaNewell, Sue. "Enhancing Cross-Project Learning". Engineering Management Journal 16, nr 1 (marzec 2004): 12–20. http://dx.doi.org/10.1080/10429247.2004.11415234.
Pełny tekst źródłaPetersen, Maya L., Annette M. Molinaro, Sandra E. Sinisi i Mark J. van der Laan. "Cross-validated bagged learning". Journal of Multivariate Analysis 98, nr 9 (październik 2007): 1693–704. http://dx.doi.org/10.1016/j.jmva.2007.07.004.
Pełny tekst źródłaNayan, Surina, Hariharan N. Krishnasamy i 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.
Pełny tekst źródłaNie, Weizhi, Anan Liu, Wenhui Li i Yuting Su. "Cross-view action recognition by cross-domain learning". Image and Vision Computing 55 (listopad 2016): 109–18. http://dx.doi.org/10.1016/j.imavis.2016.04.011.
Pełny tekst źródłaEliawati, 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.
Pełny tekst źródłaHan, Pi-Chi, i John A. Henschke. "Cross-Cultural Learning and Mentoring". International Journal of Adult Vocational Education and Technology 3, nr 3 (lipiec 2012): 26–36. http://dx.doi.org/10.4018/javet.2012070103.
Pełny tekst źródłaBonometti, Stefano. "Learning in Cross-Media Environment". International Journal of Web-Based Learning and Teaching Technologies 12, nr 4 (październik 2017): 48–57. http://dx.doi.org/10.4018/ijwltt.2017100105.
Pełny tekst źródłaMiller, Anne. "Design for cross-cultural learning". International Journal of Intercultural Relations 12, nr 3 (styczeń 1988): 296–97. http://dx.doi.org/10.1016/0147-1767(88)90022-3.
Pełny tekst źródłaRozprawy doktorskie na temat "Cross-learning"
Zhang, Li. "Cross-view learning". Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43185.
Pełny tekst źródłaSi, Si, i 斯思. "Cross-domain subspace learning". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2010. http://hub.hku.hk/bib/B44912912.
Pełny tekst źródłaHjelm, 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.
Pełny tekst źródłaFör 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.
Pełny tekst źródłaFohlin, 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.
Pełny tekst źródłaKodirov, Elyor. "Cross-class transfer learning for visual data". Thesis, Queen Mary, University of London, 2017. http://qmro.qmul.ac.uk/xmlui/handle/123456789/31852.
Pełny tekst źródłaPorto, 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/.
Pełny tekst źródłaModelos 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.
Pełny tekst źródłaSpecifying 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.
Pełny tekst źródłaNerantzi, 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.
Pełny tekst źródłaKsiążki na temat "Cross-learning"
Korhonen, Vesa. Cross-cultural lifelong learning. Tampere: Tampere University Press, 2010.
Znajdź pełny tekst źródłaStephen, Bochner, Brislin Richard W. 1945-, Lonner Walter J i East-West Culture Learning Institute, red. Cross-cultural perspectives on learning. Ann Arbor, Mich: University Microfilms International, 1987.
Znajdź pełny tekst źródłaSikkema, Mildred. Design for cross-cultural learning. Yarmouth, Me: Intercultural Press, 1987.
Znajdź pełny tekst źródła1937-, Berendt Erich Adalbert, red. Metaphors for learning: Cross-cultural perspectives. Amsterdam: John Benjamins Publishing, 2008.
Znajdź pełny tekst źródłaWestwood, Peter S. Teaching and learning difficulties: Cross-curricular perspectives. Camberwell, Vic: ACER Press, 2006.
Znajdź pełny tekst źródłaZhu, Sijia Cynthia, Shu Xie, Yunpeng Ma i Douglas McDougall, red. Reciprocal Learning for Cross-Cultural Mathematics Education. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56838-2.
Pełny tekst źródłaDavis, Sarah H. Being there: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.
Znajdź pełny tekst źródłaRingbom, Håkan. Cross-linguistic similarity in foreign language learning. Clevedon [England]: Multilingual Matters, 2007.
Znajdź pełny tekst źródłaDavis, Sarah H. Resident aliens: Learning to live cross-culturally. Cambridge, Mass: Harvard University Press, 2011.
Znajdź pełny tekst źródłaYihong, Fan, red. Assuring university learning quality: Cross-boundary collaboration. Trondheim: Tapir Academic Press, 2006.
Znajdź pełny tekst źródłaCzęści książek na temat "Cross-learning"
Delaney, Laurel J. "Cross-Cultural Learning". W Exporting, 413–22. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5792-9_24.
Pełny tekst źródłaSkocaj, Danijel, Ales Leonardis i Geert-Jan M. Kruijff. "Cross-Modal Learning". W 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.
Pełny tekst źródłaDelaney, Laurel J. "Cross-Cultural Learning". W Exporting, 451–61. Berkeley, CA: Apress, 2016. http://dx.doi.org/10.1007/978-1-4842-2193-8_24.
Pełny tekst źródłaSmith, Andrew D. M., i Kenny Smith. "Cross-Situational Learning". W 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.
Pełny tekst źródłaHibbert, Liesel, i Gregory Kerr. "Cross-disciplinary learning". W English as a Language of Learning, Teaching and Inclusivity, 143–54. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003382645-9.
Pełny tekst źródłaSchaffer, Scott P. "Cross-Disciplinary Team Learning". W 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.
Pełny tekst źródłaSchaffer, Scott P. "Cross-Disciplinary Team Learning". W 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.
Pełny tekst źródłaApfelthaler, Gerhard. "Cross-Cultural Learning Styles". W 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.
Pełny tekst źródłaLaFever, Marcella. "Cross-Cultural Learning Styles". W 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.
Pełny tekst źródłaYao, Yuan, Zhiyuan Liu, Yankai Lin i Maosong Sun. "Cross-Modal Representation Learning". W 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.
Pełny tekst źródłaStreszczenia konferencji na temat "Cross-learning"
Fisch, Shalom M., Richard Lesh, Beth Motoki, Sandra Crespo i Vincent Melfi. "Cross-platform learning". W the 10th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/1999030.1999036.
Pełny tekst źródłaFu, Eugene Yujun, Michael Xuelin Huang, Hong Va Leong i Grace Ngai. "Cross-Species Learning". W MM '18: ACM Multimedia Conference. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3240508.3240710.
Pełny tekst źródłaKang, Cuicui, Shengcai Liao, Yonghao He, Jian Wang, Wenjia Niu, Shiming Xiang i Chunhong Pan. "Cross-Modal Similarity Learning". W 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.
Pełny tekst źródłaM’hamdi, Meryem, Xiang Ren i Jonathan May. "Cross-lingual Continual Learning". W 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.
Pełny tekst źródłaCervino, Juan, Juan Andres Bazerque, Miguel Calvo-Fullana i Alejandro Ribeiro. "Multi-task Supervised Learning via Cross-learning". W 2021 29th European Signal Processing Conference (EUSIPCO). IEEE, 2021. http://dx.doi.org/10.23919/eusipco54536.2021.9615939.
Pełny tekst źródłaWang, Yabing, Jianfeng Dong, Tianxiang Liang, Minsong Zhang, Rui Cai i Xun Wang. "Cross-Lingual Cross-Modal Retrieval with Noise-Robust Learning". W MM '22: The 30th ACM International Conference on Multimedia. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3503161.3548003.
Pełny tekst źródłaJohnson, Andrew, Penny Karanasou, Judith Gaspers i Dietrich Klakow. "Cross-lingual Transfer Learning for". W 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.
Pełny tekst źródłaRuder, Sebastian, Anders Søgaard i Ivan Vulić. "Unsupervised Cross-Lingual Representation Learning". W 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.
Pełny tekst źródłaLiu, Alexander, SouYoung Jin, Cheng-I. Lai, Andrew Rouditchenko, Aude Oliva i James Glass. "Cross-Modal Discrete Representation Learning". W 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.
Pełny tekst źródłaMao, WeiYang, i jshardrom xia. "Cross-modal representation learning based on contrast learning". W 4th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2022), redaktorzy Mengyi (Milly) Cen i Lidan Wang. SPIE, 2022. http://dx.doi.org/10.1117/12.2640128.
Pełny tekst źródłaRaporty organizacyjne na temat "Cross-learning"
Klenk, Matthew, i Ken Forbus. Cross Domain Analogies for Learning Domain Theories. Fort Belvoir, VA: Defense Technical Information Center, styczeń 2007. http://dx.doi.org/10.21236/ada471251.
Pełny tekst źródłaGarcía Betegón, Mercedes, Eva Perandones Serrano i Francisco Javier Gayo Santacecilia. Cross-cutting methodologies in learning 3D modeling. Peeref, kwiecień 2023. http://dx.doi.org/10.54985/peeref.2304p9515916.
Pełny tekst źródłaMcCloskey, Michael J., Kyle J. Behymer, Elizabeth L. Papautsky i Aniko Grandjean. Measuring Learning and Development in Cross-Cultural Competence. Fort Belvoir, VA: Defense Technical Information Center, wrzesień 2012. http://dx.doi.org/10.21236/ada568555.
Pełny tekst źródłaFreed, Danielle. K4D Strengthening Cross-sector Learning for Education and FCAS. Institute of Development Studies, wrzesień 2022. http://dx.doi.org/10.19088/k4d.2022.159.
Pełny tekst źródłaWang, Zhe, Chao Fan, Xian Min, Shoukun Sun, Xiaogang Ma i Xiang Que. Cross-scale Urban Land Cover Mapping: Empowering Classification through Transfer Learning and Deep Learning Integration. Purdue University, październik 2023. http://dx.doi.org/10.5703/1288284317663.
Pełny tekst źródłaThrun, Sebastian, i Joseph O'Sullivan. Clustering Learning Tasks and the Selective Cross-Task Transfer of Knowledge,. Fort Belvoir, VA: Defense Technical Information Center, listopad 1995. http://dx.doi.org/10.21236/ada303253.
Pełny tekst źródłaShevtsiv, Nikita A., i Andrii M. Striuk. Cross platform development vs native development. CEUR Workshop Proceedings, marzec 2021. http://dx.doi.org/10.31812/123456789/4428.
Pełny tekst źródłaSakurauchi, Yoko. Teaching and Learning for Intercultural Sensitivity: A Cross-Cultural Examination of American Domestic Students and Japanese Exchange Students. Portland State University Library, styczeń 2000. http://dx.doi.org/10.15760/etd.1642.
Pełny tekst źródłaChen, Yunxiang, Jie Bao, Jianqiu Zheng, Peiyuan Gao, Qizhi He, James Stegen, Brenda Ng, Xiaofeng Liu, Roman Dibiase i 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), kwiecień 2021. http://dx.doi.org/10.2172/1769792.
Pełny tekst źródłaFreed, Danielle. K4D Learning Journey Strengthens the Mainstreaming of Water Security. Institute of Development Studies, wrzesień 2022. http://dx.doi.org/10.19088/k4d.2022.164.
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