Academic literature on the topic 'Training data recommendation'
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Journal articles on the topic "Training data recommendation":
Komurlekar, Runali. "Movie Recommendation Model from Data through Online Streaming." International Journal for Research in Applied Science and Engineering Technology 9, no. 8 (August 31, 2021): 1549–51. http://dx.doi.org/10.22214/ijraset.2021.37495.
Adnan, Muhammad, Yassaman Ebrahimzadeh Maboud, Divya Mahajan, and Prashant J. Nair. "Accelerating recommendation system training by leveraging popular choices." Proceedings of the VLDB Endowment 15, no. 1 (September 2021): 127–40. http://dx.doi.org/10.14778/3485450.3485462.
Wang, Qingren, Min Zhang, Tao Tao, and Victor S. Sheng. "Labelling Training Samples Using Crowdsourcing Annotation for Recommendation." Complexity 2020 (May 5, 2020): 1–10. http://dx.doi.org/10.1155/2020/1670483.
劉怡, 劉怡. "Research of Art Point of Interest Recommendation Algorithm Based on Modified VGG-16 Network." 電腦學刊 33, no. 1 (February 2022): 071–85. http://dx.doi.org/10.53106/199115992022023301008.
Daniel, Thomas, Fabien Casenave, Nissrine Akkari, and David Ryckelynck. "Data Augmentation and Feature Selection for Automatic Model Recommendation in Computational Physics." Mathematical and Computational Applications 26, no. 1 (February 16, 2021): 17. http://dx.doi.org/10.3390/mca26010017.
Salenko, A. A., and E. V. Morar. "DESIGN AND DEVELOPMENT OF A MOVIE RECOMMENDATION SERVICE." Applied Mathematics and Fundamental Informatics 8, no. 2 (2021): 046–53. http://dx.doi.org/10.25206/2311-4908-2021-8-1-46-53.
Xu, Gaochao, Yan Ding, Yuqiang Jiang, Ming Hu, and Jia Zhao. "A Novel Distributed Recommendation Framework Using Big Data in Social Context." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 08 (May 9, 2017): 1759015. http://dx.doi.org/10.1142/s0218001417590157.
Zhang, Heng-Ru, Fan Min, and Xu He. "Aggregated Recommendation through Random Forests." Scientific World Journal 2014 (2014): 1–11. http://dx.doi.org/10.1155/2014/649596.
Nanry, Charles. "Performance Linked Training." Public Personnel Management 17, no. 4 (December 1988): 457–63. http://dx.doi.org/10.1177/009102608801700409.
Zamani, Hamed. "Neural models for information retrieval without labeled data." ACM SIGIR Forum 53, no. 2 (December 2019): 104–5. http://dx.doi.org/10.1145/3458553.3458569.
Dissertations / Theses on the topic "Training data recommendation":
Labiadh, Mouna. "Méthodologie de construction de modèles adaptatifs pour la simulation énergétique des bâtiments." Thesis, Lyon, 2021. http://www.theses.fr/2021LYSE1158.
Predictive modeling of energy consumption in buildings is essential for intelligent control and efficient planning of energy networks. One way to perform predictive modeling is through machine learning approaches. Alongside their good performance, these approaches are time efficient and facilitates the integration of buildings into smart environments. However, accurate machine learning models rely heavily on collecting relevant building operational data in a sufficient amount, notably when deep learning is used. In the field of buildings energy, historical data are not available for training, such is the case in newly built or newly renovated buildings. Moreover, it is common to verify the energy efficiency of buildings before construction or renovation. For such cases, only a contextual description about the future building and its design is available. The goal of this dissertation is to address the predictive modeling tasks of building energy consumption when no historical data are available for the given target building. To that end, existing data collected from multiple different source buildings are leveraged. This is increasingly relevant with the growth of open data initiatives in various sectors, namely building energy. The main idea is to transfer knowledge across building models. There is little research at the intersection of building energy modeling and knowledge transfer. An important challenge arises when dealing with multi-source data, since large domain shift may exist between different sources and also between each source and the target. As a contribution, a two-fold query-adaptive methodology is developed for cross-building predictive modeling. The first process recommends relevant training data to a target building solely by using a minimal contextual description on it (metadata). Contextual descriptions are provided as user queries. To enable a task-specific recommendation, a deep similarity learning framework is used. The second process trains multiple predictive models based on recommended training data. These models are combined together using an ensemble learning framework to ensure a robust performance. The implementation of the proposed methodology is based on microservices. Logically independent workflows are modeled as microservices with single purposes and separate data sources. Building metadata and time series data collected from multiple sources are integrated into an unified ontology-based view. Experimental evaluation of the predictive model factory validates the effectiveness and the applicability for the use case of building energy modeling. Moreover, because of its generic design, the methodology for query-adaptive cross-domain predictive modeling can be re-used for a diverse range of use cases in different fields
Books on the topic "Training data recommendation":
Program, CIMMYT Economics. From agronomic data to farmer recommendations: An economics training manual. México, D.F., México: CIMMYT Economics Program, 1988.
Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Varlamov, Oleg. Fundamentals of creating MIVAR expert systems. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1513119.
Mandra, Yuliya, Elena Semencova, Sergey Griroriev, N. Gegalina, Elena Svetlakova, Maria Vlasova, Yuriy Boldyrev, Anastasiya Kotikova, Aleksandr Ivashov, and Aleksandr Legkih. MODERN METHODS OF COMPLEX TREATMENT OF PATIENTS WITH HERPES SIMPLEX LIPS. ru: TIRAZH Publishing House, 2019. http://dx.doi.org/10.18481/textbook_5dfa340500ebf6.85792235.
Basovskiy, Leonid, and Elena Basovskaya. Fundamentals of scientific research. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1192099.
Naumov, Vladimir. Consumer behavior. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1014653.
Office, General Accounting. Financial management: Recommendations on Indian trust fund Strategic Plan proposals : report to the Secretary of the Interior. Washington, D.C. (P.O. Box 37050, Washington, D.C. 20013): The Office, 1997.
Dallmeijer, Annet, and Jost Schnyder. Exercise capacity and training in cerebral palsy and other neuromuscular diseases. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199232482.003.0035.
Bellosta-López, Pablo, Priscila de Brito Silva, Palle S. Jensen, Morten S. Hoegh, Thorvaldur S. Palsson, Steffan Wittrup Mc Phee Christensen, Julia Blasco-Abadía, et al. Recommendations for implementation of the topic musculoskeletal disorders in the occupational health and safety postgraduate programmes at European Universities. Prevent4Work, 2021. http://dx.doi.org/10.54391/123456789/672.
Nahir, Menachem, Doron Zahger, and Yonathan Hasin. Recommendations for the structure, organization, and operation of intensive cardiac care units. Oxford University Press, 2015. http://dx.doi.org/10.1093/med/9780199687039.003.0010.
Book chapters on the topic "Training data recommendation":
Sun, Hao, Yunzhuo Wang, Jingwei Sun, and Guangzhong Sun. "Fast Training of POI Recommendation Models Using Gradient Compression." In Spatial Data and Intelligence, 72–86. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69873-7_6.
Pohlmann, Andreas, Susan J. Back, Andrea Fekete, Iris Friedli, Stefanie Hectors, Neil Peter Jerome, Min-Chi Ku, et al. "Recommendations for Preclinical Renal MRI: A Comprehensive Open-Access Protocol Collection to Improve Training, Reproducibility, and Comparability of Studies." In Methods in Molecular Biology, 3–23. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_1.
Levy, Raymond A., and Milton Kotelchuck. "Fatherhood and Reproductive Health in the Antenatal Period: From Men’s Voices to Clinical Practice." In Engaged Fatherhood for Men, Families and Gender Equality, 111–37. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75645-1_6.
Prosvetov, A. V. "Using the Generative Adversarial Network to Generate Recommendations." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200680.
Xing, Hao, Zhike Han, and Yichen Shen. "ClothNet: A Neural Network Based Recommender System." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200706.
S., Aravindha Ramanan. "Recommender System Techniques and Approaches to Improve the Modern Learning Challenges." In Machine Learning Approaches for Improvising Modern Learning Systems, 114–43. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-5009-0.ch005.
Goncalves, Marlene, Patrick Rengifo, Daniela Andreina Rodríguez, and Ivette C. Martínez. "A Route Recommender System Based on Current and Historical Crowdsourcing." In Social Media Data Extraction and Content Analysis, 114–36. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0648-5.ch005.
Chen, Shaoquan, Jianping Cai, and Lan Sun. "Rényi Differential Privacy Protection Algorithm for SVD Recommendation Model." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2021. http://dx.doi.org/10.3233/faia210171.
Tu, Zhiwen, Yawen Yin, and Xianan Qin. "Towards Better Data Pre-Processing for Building Recipe Recommendation Systems from Industrial Fabric Dyeing Manufacturing Records: Categorization of Coloration Properties for a Dye Combination on Different Fabrics." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2022. http://dx.doi.org/10.3233/faia220006.
Kari, Tuomas, Miia Siutila, and Veli-Matti Karhulahti. "An Extended Study on Training and Physical Exercise in Esports." In Exploring the Cognitive, Social, Cultural, and Psychological Aspects of Gaming and Simulations, 270–92. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-7461-3.ch010.
Conference papers on the topic "Training data recommendation":
Srivastava, Rajiv, Girish Keshav Palshikar, and Saheb Chourasia. "What's Next? A Recommendation System for Industrial Training." In 2017 IEEE International Conference on Data Mining Workshops (ICDMW). IEEE, 2017. http://dx.doi.org/10.1109/icdmw.2017.35.
Yu, Junliang, Hongzhi Yin, Min Gao, Xin Xia, Xiangliang Zhang, and Nguyen Quoc Viet Hung. "Socially-Aware Self-Supervised Tri-Training for Recommendation." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467340.
Yu, Junliang, Min Gao, Hongzhi Yin, Jundong Li, Chongming Gao, and Qinyong Wang. "Generating Reliable Friends via Adversarial Training to Improve Social Recommendation." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00087.
Li, Xiangkun, and Fenghao Sun. "Sports Training Recommendation Method under the Background of Data Analysis." In 2021 International Conference on High Performance Big Data and Intelligent Systems (HPBD&IS). IEEE, 2021. http://dx.doi.org/10.1109/hpbdis53214.2021.9658481.
Huang, Yuzhen, Xiaohan Wei, Xing Wang, Jiyan Yang, Bor-Yiing Su, Shivam Bharuka, Dhruv Choudhary, Zewei Jiang, Hai Zheng, and Jack Langman. "Hierarchical Training: Scaling Deep Recommendation Models on Large CPU Clusters." In KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3447548.3467084.
Lo, Kachun, and Tsukasa Ishigaki. "Matching Novelty While Training: Novel Recommendation Based on Personalized Pairwise Loss Weighting." In 2019 IEEE International Conference on Data Mining (ICDM). IEEE, 2019. http://dx.doi.org/10.1109/icdm.2019.00057.
Yuan, Fajie, Xiangnan He, Haochuan Jiang, Guibing Guo, Jian Xiong, Zhezhao Xu, and Yilin Xiong. "Future Data Helps Training: Modeling Future Contexts for Session-based Recommendation." In WWW '20: The Web Conference 2020. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3366423.3380116.
Ding, Jingtao, Yuhan Quan, Xiangnan He, Yong Li, and Depeng Jin. "Reinforced Negative Sampling for Recommendation with Exposure Data." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/309.
Pang, Guangyao, Xiaoying Zhu, Keda Lu, Zizhen Peng, and Weitao Deng. "A simulator for reinforcement learning training in the recommendation field." In 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, 2020. http://dx.doi.org/10.1109/ispa-bdcloud-socialcom-sustaincom51426.2020.00156.
Liu, Zhiyong, and Junping Zhang. "Research on Key Algorithms of Intelligent Recommendation System for Retired soldiers’ Employment Training." In BDSIC 2021: 2021 3rd International Conference on Big-data Service and Intelligent Computation. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3502300.3502308.
Reports on the topic "Training data recommendation":
Gehlhaus, Diana, Luke Koslosky, Kayla Goode, and Claire Perkins. U.S. AI Workforce: Policy Recommendations. Center for Security and Emerging Technology, October 2021. http://dx.doi.org/10.51593/20200087.
Guest, Arlene A., Peter S. Guest, Paul A. Frederickson, and Tom Murphree. Evaluation of JSAF EM Propagation Prediction Methods for Navy Continuous Training Environment/Fleet Synthetic Training, Results and Recommendations: Part 3 - An Overview of JSAF's Environmental Capabilities and Data. Fort Belvoir, VA: Defense Technical Information Center, December 2012. http://dx.doi.org/10.21236/ada570940.
Haddock, John E., Reyhaneh Rahbar-Rastegar, M. Reza Pouranian, Miguel Montoya, and Harsh Patel. Implementing the Superpave 5 Asphalt Mixture Design Method in Indiana. Purdue University, 2020. http://dx.doi.org/10.5703/1288284317127.
Korobeinikova, Tetiana I., Nataliia P. Volkova, Svitlana P. Kozhushko, Daryna O. Holub, Nataliia V. Zinukova, Tetyana L. Kozhushkina, and Sergei B. Vakarchuk. Google cloud services as a way to enhance learning and teaching at university. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3854.
Braun, Lindsay, Jesus Barajas, Bumsoo Lee, Rebecca Martin, Rafsun Mashraky, Shubhangi Rathor, and Manika Shrivastava. Construction of Pedestrian Infrastructure along Transit Corridors. Illinois Center for Transportation, March 2021. http://dx.doi.org/10.36501/0197-9191/21-004.
Perera, Duminda, Ousmane Seidou, Jetal Agnihotri, Mohamed Rasmy, Vladimir Smakhtin, Paulin Coulibaly, and Hamid Mehmood. Flood Early Warning Systems: A Review Of Benefits, Challenges And Prospects. United Nations University Institute for Water, Environment and Health, August 2019. http://dx.doi.org/10.53328/mjfq3791.
McKenna, Patrick, and Mark Evans. Emergency Relief and complex service delivery: Towards better outcomes. Queensland University of Technology, June 2021. http://dx.doi.org/10.5204/rep.eprints.211133.
Gendered effects of COVID-19 school closures: Pakistan case study. Population Council, 2022. http://dx.doi.org/10.31899/sbsr2022.1002.