Relevant bibliographies by topics / Combined Learning

Contents

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

Academic literature on the topic 'Combined Learning'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Combined Learning"

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Jovita Lantang Anetha, Gisela. "Development of a Discovery Learning Learning Model Combined with Literacy Activities and Educandy Games in Learning Mathematics." International Journal of Science and Research (IJSR) 12, no. 6 (2023): 1850–57. http://dx.doi.org/10.21275/sr23612160341.

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Astuti, Irnin Agustina Dwi, Yoga Budi Bhakti, Ria Asep Sumarni, Dwi Sulisworo, and Moh Toifur. "FLIPPED CLASSROOM AND KAHOOT IN PHYSICS LEARNING: IMPROVING STUDENTS’ MOTIVATION LEARNING." International Journal of Educational Management and Innovation 2, no. 2 (2021): 175. http://dx.doi.org/10.12928/ijemi.v2i2.3491.

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Ideal condition in physics learning should combine concepts with technology. Traditional physics learning causes students to be not interested in learning physics. There must be innovations in physics learning to be more interactive learning. One of them flipped classroom learning. Flipped classroom learning combined with Kahoot online games will make students more active. The purpose of this research is to increase students' learning motivation with Kahoot-based flipped classroom learning. The method used in this research is the quasi-experimental method with the research design of The Random
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Bazarbayeva, Laura Taituleuvna, Irina Viktorovna Lapina, and Gulfiya Faissovna Gainutdinova. "Combined learning: realities and prospects." International Journal of Professional Science, no. 8 (2021): 12–16. http://dx.doi.org/10.54092/25421085_2021_8_12.

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Furse, C. "Teaching and learning combined (TLC)." IEEE Antennas and Propagation Magazine 45, no. 3 (2003): 166–67. http://dx.doi.org/10.1109/map.2003.1232181.

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Agafonov, A. Yu, S. N. Burmistrov, D. D. Kozlov, and A. P. Kryukova. "Implicit Learning of Combined Sequences." Integration of Education 22, no. 2 (2018): 339–52. http://dx.doi.org/10.15507/1991-9468.091.022.201802.339-352.

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P. J., Anu, and K. Ranjith Singh. "Ensemble and deep learning via median method for learning disability classification." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2031–41. https://doi.org/10.11591/eei.v14i3.8639.

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The study explores the classification of students with and without learning disabilities (LD) through machine learning techniques, utilizing a real dataset and implementing bootstrapping for data augmentation. Noteworthy findings reveal the Adam optimizer's superior performance among various optimizers, achieving a true positive rate (TPR) of 0.97 and a false positive rate (FPR) of 0.02, with high precision, recall, and f1-score values. Additionally, ensemble learning, employing the median method, combines models like Random-ForestClassifier and KerasClassifier, and BaggingClassifier with Kera
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Emima, A., and D. I. George Amalarethinam. "Integrative Ensemble Learning Algorithm for Predicting Students’ Performance." Indian Journal Of Science And Technology 18, no. 1 (2025): 72–84. https://doi.org/10.17485/ijst/v18i1.3718.

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Objectives: To create a stable student performance prediction model utilizing ensemble learning methods. Methods: The study uses boosting techniques such as CatBoost, Extreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM) as simple classifiers, which are then combined into a composite classifier to improve predictive accuracy. During the training phase, a 5-level hyperparameter optimization for the basic classifiers is performed using ETLBO Optimization IELA's distinguishing feature is its Stacking ensemble method, which functions as an ensemble technique, combinin
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Francois-Lavet, Vincent, Yoshua Bengio, Doina Precup, and Joelle Pineau. "Combined Reinforcement Learning via Abstract Representations." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3582–89. http://dx.doi.org/10.1609/aaai.v33i01.33013582.

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In the quest for efficient and robust reinforcement learning methods, both model-free and model-based approaches offer advantages. In this paper we propose a new way of explicitly bridging both approaches via a shared low-dimensional learned encoding of the environment, meant to capture summarizing abstractions. We show that the modularity brought by this approach leads to good generalization while being computationally efficient, with planning happening in a smaller latent state space. In addition, this approach recovers a sufficient low-dimensional representation of the environment, which op
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Talaghzi, Jallal, Mostafa Bellafkih, Abdellah Bennane, Mohammed Majid Himmi, and Mohammed Amraouy. "Combined E-Learning Course Recommender System." International Journal of Emerging Technologies in Learning (iJET) 18, no. 06 (2023): 53–70. http://dx.doi.org/10.3991/ijet.v18i06.36987.

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In this paper, we propose learners course recommender system of the E-Dirassa platform. This system, which distinguishes between new and active learners, adopts three complementary recommendation approaches: content-based recommendation as well as course-based collaborative filtering for active learners, and static profile-based collaborative filtering for new learners. Let us also note the use of multi-criteria decision aid (MDA) techniques for the choice of the textual similarity measure to be used in the first approach of content-based recommendation.
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ASKER, LARS, MATS DANIELSON, and LOVE EKENBERG. "COMMITTEES OF LEARNING AGENTS." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 08, no. 02 (2000): 187–202. http://dx.doi.org/10.1142/s0218488500000137.

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We describe how machine learning and decision theory is combined in an application that supports control room operators of a combined heating and power plant to cope with the overwhelming complexity of situations when severe plant disturbances occur. The application is designed as an assistant, rather than as an automatic system that intervenes directly in the operator/plant loop. The application is required to handle vague and numerically imprecise background information in the construction of classifier committees. A classifier committee (or ensemble) is a classifier created by combining the
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Dissertations / Theses on the topic "Combined Learning"

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Paulus, Kyle. "Combined CALPHAD and Machine Learning for Property Modelling." Thesis, KTH, Materialvetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-278149.

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Techniques to improve the speed at which materials are researched and developed has been conducted by investigating the machine learning methodology. These techniques offer solutions to connect the length scales of material prop- erties from atomistic and chemical features using materials databases generated from collected data. In this assessment, two material informatics methodologies are used to predict material properties in steels and nickel based superalloys using this approach. Martensite start temperature and sigma phase amount as a function of input composition has been modelled with
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Olsen, Jennifer. "Orchestrating Combined Collaborative and Individual Learning in the Classroom." Research Showcase @ CMU, 2017. http://repository.cmu.edu/dissertations/1000.

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In the classroom, teachers make use of different combinations of social planes (e.g., individual, collaborative) to support learning. However, little is known about the complementary strengths of individual and collaborative learning or how to combine them so that they are more effective than either social plane alone. One roadblock to this investigation is an ability to orchestrate, or manage, more complex, but theoretically effective, combinations of collaborative and individual learning in the classroom. Prior research has created orchestration tools that support the planning and real-time
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Ko, E. Soon. "Product Matching through Multimodal Image and Text Combined Similarity Matching." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-301306.

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Product matching in e-commerce is an area that faces more and more challenges with growth in the e-commerce marketplace as well as variation in the quality of data available online for each product. Product matching for e-commerce provides competitive possibilities for vendors and flexibility for customers by identifying identical products from different sources. Traditional methods in product matching are often conducted through rule-based methods and methods tackling the issue through machine learning usually do so through unimodal systems. Moreover, existing methods would tackle the issue t
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Bhatt, Dhruv. "Economic Dispatch of the Combined Cycle Power Plant Using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-266110.

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Combined Cycle Power Plant (CCPP)s play a key role in modern powersystem due to their lesser investment cost, lower project executiontime, and higher operational flexibility compared to other conventionalgenerating assets. The nature of generation system is changing withever increasing penetration of the renewable energy resources. Whatwas once a clearly defined generation, transmission, and distributionflow is shifting towards fluctuating distribution generation. Because ofvariation in energy production from the renewable energy resources,CCPP are increasingly required to vary their load leve
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Thornton, Chris. "Auto-WEKA : combined selection and hyperparameter optimization of supervised machine learning algorithms." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46177.

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Many different machine learning algorithms exist; taking into account each algorithm's set of hyperparameters, there is a staggeringly large number of possible choices. This project considers the problem of simultaneously selecting a learning algorithm and setting its hyperparameters. Previous works attack these issues separately, but this problem can be addressed by a fully automated approach, in particular by leveraging recent innovations in Bayesian optimization. The WEKA software package provides an implementation for a number of feature selection and supervised machine learning algorithms
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Desypris, Georgios A. "Enhancement of learning process in web-based courses using combined media components." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2002. http://library.nps.navy.mil/uhtbin/hyperion-image/02sep%5FDesypris.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2002.<br>Thesis advisor(s): Rudolph Darken, Anthony Ciavarelli. Includes bibliographical references (p. 85-86). Also available online.
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Lombard, Valentin. "Geometric deep manifold learning combined with natural language processing for protein movies." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS379.

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Les protéines jouent un rôle central dans les processus biologiques, et comprendre comment elles se déforment et se déplacent est essentiel pour élucider leurs mécanismes fonctionnels. Malgré les récentes avancées dans les technologies à haut débit, qui ont élargi nos connaissances sur les structures protéiques, la prédiction précise de leurs différents états conformationnels et mouvements reste un défi majeur. Nous présentons deux approches complémentaires pour relever le défi de la compréhension et de la prédiction de l'ensemble de la variabilité conformationnelle des protéines. La première
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Agarwal, Akrita. "Exploring the Noise Resilience of Combined Sturges Algorithm." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447070335.

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Mazzitelli, Carla. "Aprendizado motor em escolares: comparação entre prática mental, prática física e prática combinada." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/47/47135/tde-28062013-142756/.

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A compreensão do processo de aprendizagem motora é de real importância para a atividade fisioterapêutica, visto que as abordagens e técnicas visam à mudança e o aperfeiçoamento de uma atividade motora ou mesmo a aquisição de uma nova habilidade. Neste processo a prática realizada tem implicação direta na aprendizagem motora. Objetivo: comparar os efeitos da prática combinada (iniciada pelo treino mental, seguido pelo treino físico, com distribuição proporcionada entre os mesmos), com os efeitos da prática mental e física isoladas, sobre aquisição, retenção, transferência inter tarefa e inter m
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Carls, Fredrik. "Evaluation of machine learning methods for anomaly detection in combined heat and power plant." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255006.

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In the hope to increase the detection rate of faults in combined heat and power plant boilers thus lowering unplanned maintenance three machine learning models are constructed and evaluated. The algorithms; k-Nearest Neighbor, One-Class Support Vector Machine, and Auto-encoder have a proven track record in research for anomaly detection, but are relatively unexplored for industrial applications such as this one due to the difficulty in collecting non-artificial labeled data in the field.The baseline versions of the k-Nearest Neighbor and Auto-encoder performed very similarly. Nevertheless, the
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Books on the topic "Combined Learning"

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Combined Jewish Philanthropies of Greater Boston. Bring learning to life: With Barry Shrage and friends, April 6, 2014 ... Program. Combined Jewish Philanthropies of Greater Boston, 2014.

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Okogbe, Tola Patrick. The effect of a combined motor and learning activities on the performance of language (spelling) and arithmeticof children with moderate learning difficulties. University of Birmingham, 1994.

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Caldwell, Phoebe. Finding you finding me: Using intensive interaction to get in touch with people with severe learning disabilities combined with autistic spectrum disorder. Jessica Kingsley, 2005.

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McMahon, Anna C. Circle time and self-organised learning: The development and evaluation of a combined programme focusing on classroom behaviours. University of Birmingham, 2000.

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Educational Resources Information Center (U.S.), ed. Preparation of leadership personnel: Combined deaf education/learning disabilities : final performance report : budget and award year, 1993-1997. Dept. of Hearing and Speech, the University of Kansas Medical Center, 1997.

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Bob, Snow, and Sense (Organisation), eds. Making sense of the world: A guide for carers working with people who have combined sensory and learning disabilities. Sense, National Deaf-Blind and Rubella Association, 1994.

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Lainsbury, Tracy-Lynn. R.I.P.E.: Combined devices for facilitating verbal learning and memory recall : the effects of repetition, imagery, position and emotion on memory for words. Laurentian University, Department of Psychology, 1995.

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Dillon, J. T. Personal teaching: Efforts to combine personal love and professional skill in the classroom. University Press of America, 1990.

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Torlone, Francesca, and Marios Vryonides, eds. Innovative learning models for prisoners. Firenze University Press, 2016. http://dx.doi.org/10.36253/978-88-6655-924-5.

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Prison education should be a top priority issue in most societies. Prison conditions must not infringe human rights and dignity and must offer meaningful treatment programmes in order to support inmates in their rehabilitation and reintegration in society. The use of ICTs within a penitentiary context plays a crucial role in that. The present Volume looks at the learning potential in prisons and reports on innovative (e-)learning pathways for basic skills education as designed and tested in Cyprus, Greece, Italy and Romania. Research investigated on what counts as ‘educational’ in such a compl
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Perrett, Jeanne. Little Learning Stars Pupil's and Activity Book combined. Macmillan Education, 2014.

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Book chapters on the topic "Combined Learning"

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Herbig, Tobias, Franz Gerl, and Wolfgang Minker. "Combined Speaker Adaptation." In Self-Learning Speaker Identification. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19899-1_4.

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Owens, David H. "Combined Inverse and Gradient Based Design." In Iterative Learning Control. Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6772-3_8.

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Choi, Kwansun, Saeron Han, Sunghwan Kim, et al. "A Combined Virtual and Remote Laboratory for Microcontroller." In Hybrid Learning and Education. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03697-2_7.

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Xu, Han-yang, and Feng Zhou. "Sparse Representation Based SAR Imaging Using Combined Dictionary." In Machine Learning and Intelligent Communications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-73447-7_15.

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Ryckelynck, David, Fabien Casenave, and Nissrine Akkari. "Structured Data and Knowledge in Model-Based Engineering." In Manifold Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-52764-7_1.

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AbstractModel-based engineering refers to the applied mathematical methods and tools used in the industry in the design processes. In this chapter we introduce how geometrical, thermal and mechanical models are used and combined in complex systems. These models are implemented in computer platforms. They generate structured data that enable engineers to design future products.
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Henmi, Kohei, and Motonobu Hattori. "Parallel Learning for Combined Knowledge Acquisition Model." In Neural Information Processing. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46687-3_4.

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Pei, Weiping, Youye Xie, and Gongguo Tang. "Spammer Detection via Combined Neural Network." In Machine Learning and Data Mining in Pattern Recognition. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96136-1_28.

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Panigrahi, Satya Sobhan, and Ajay Kumar Jena. "Test Scenarios Generation Using Combined Object-Oriented Models." In Learning and Analytics in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38006-9_4.

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Liu, Lu, Haiyan Jin, Junfei Shi, and Wei Liang. "PolSAR Data Classification via Combined Similarity Based Immune Clonal Spectral Clustering." In E-Learning and Games. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23712-7_22.

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Lee, Kwang Soon, and Jay H. Lee. "Model-Based Predictive Control Combined with Iterative Learning for Batch or Repetitive Processes." In Iterative Learning Control. Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5629-9_16.

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Conference papers on the topic "Combined Learning"

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Kumar, Rathina, Getzi Jeba Lellipushpam Paulraj, and Immanuel Johnraja Jebadurai. "Meta Learning combined with Federated Learning for Detecting Blackhole Attacks." In First International Conference on Computer, Computation and Communication (IC3C-2025). River Publishers, 2025. https://doi.org/10.13052/rp-9788743808268a082.

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Qiao, Rui, Xiaolei Zhong, and Yang Fu. "Combined Representation Learning for Uncertain Knowledge Graphs." In 2024 2nd International Conference on Mechatronics, IoT and Industrial Informatics (ICMIII). IEEE, 2024. http://dx.doi.org/10.1109/icmiii62623.2024.00161.

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Li, Han, and Chia-Wei Tsai. "Deep Learning Combined with Machine Learning for ON/OFF Home Appliances." In 2024 2nd International Conference on Artificial Intelligence and Power Engineering (AIPE). IEEE, 2024. https://doi.org/10.1109/aipe63225.2024.00010.

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Holk, Simon, Daniel Marta, and Iolanda Leite. "POLITE: Preferences Combined with Highlights in Reinforcement Learning." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610505.

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Zheng, JunShuai, YiChao Zhou, XiYuan Hu, and ZhenMin Tang. "Deepfake Detection With Combined Unsupervised-Supervised Contrastive Learning." In 2024 IEEE International Conference on Image Processing (ICIP). IEEE, 2024. http://dx.doi.org/10.1109/icip51287.2024.10647603.

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Luo, Yang, Jun Liu, and Mengqian Ding. "CC-Net: Consistency Learning Combined with Contrastive Learning for Kidney Ultrasound Segmentation." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10830962.

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Wang, Xinyu, Jinglin Zhou, and Haiyan Wu. "Novel MSMOTE Combined with LSDA for Imbalanced Fault Diagnosis." In 2024 IEEE 13th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2024. http://dx.doi.org/10.1109/ddcls61622.2024.10606631.

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Wang, Zelin, Xiaoli Li, and Kang Wang. "Modeling of Liquid Level Based on Combined Dynamic Neural Network." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065005.

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Erdogan, Hakan, and Mehmet Umut Sen. "A combined approach to regularized linear combiner learning." In 2010 IEEE 18th Signal Processing and Communications Applications Conference (SIU). IEEE, 2010. http://dx.doi.org/10.1109/siu.2010.5652790.

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Liu, Hui. "Learning Optimization Algorithm Combined with Psychology." In 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON). IEEE, 2023. http://dx.doi.org/10.1109/indiscon58499.2023.10270588.

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Reports on the topic "Combined Learning"

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Baras, John S., and Subhrakanti Dey. Combined Compression and Classification with Learning Vector Quantization. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada438572.

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Pazzani, Michael J. A Combined Analytic and Inductive Approach to Learning in Knowledge-based Systems. Defense Technical Information Center, 1997. http://dx.doi.org/10.21236/ada335735.

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Kulhandjian, Hovannes. Detecting Driver Drowsiness with Multi-Sensor Data Fusion Combined with Machine Learning. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2015.

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In this research work, we develop a drowsy driver detection system through the application of visual and radar sensors combined with machine learning. The system concept was derived from the desire to achieve a high level of driver safety through the prevention of potentially fatal accidents involving drowsy drivers. According to the National Highway Traffic Safety Administration, drowsy driving resulted in 50,000 injuries across 91,000 police-reported accidents, and a death toll of nearly 800 in 2017. The objective of this research work is to provide a working prototype of Advanced Driver Ass
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Osadchyi, Viacheslav V., Hanna Y. Chemerys, Kateryna P. Osadcha, Vladyslav S. Kruhlyk, Serhii L. Koniukhov, and Arnold E. Kiv. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4417.

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The article is devoted to actual problem of using modern ICT tools to increase the level of efficiency of the educational process. The current state and relevance of the use of augmented reality (AR) and virtual reality (VR) technologies as an appropriate means of improving the educational process are considered. In particular, attention is paid to the potential of the combined capabilities of AR and VR technologies with adaptive learning systems. Insufficient elaboration of cross-use opportunities for achieving of efficiency of the educational process in state-of-the-art research has been ide
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Ruzsinszky, Adrienn, Hong Tang, Santosh Neupane, et al. Optoelectronic properties of bent two-dimensional materials from first-principles methods combined with machine learning. Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2228502.

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Picho, Katherine, Timothy J. Cleary, Jr Artino, Dong Anthony R., and Ting. Developing and Testing a Self-Regulated Learning Assessment Methodology Combined with Virtual-Patient Simulation in Medical Education. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada623009.

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Canto, Patricia, ed. Learning Modes, Types of Innovation and Economic Performance. Universidad de Deusto, 2012. http://dx.doi.org/10.18543/xebj1954.

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This contribution focuses on a current heated debate on learning modes employed by the firms, and their impact on innovation and economic output. The interactive approach developed by the Scandinavian school on innovation systems characterized two key learning modes as „science and technology-based innovation? (STI) and „learning-by-doing, by-using and by-interacting-based innovation (DUI). This work analyzes first the separate and combined impact of such modes of learning and innovation on two types of innovation output: product and process. In this operation, this work produces interesting a
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Pompeu, Gustavo, and José Luiz Rossi. Real/Dollar Exchange Rate Prediction Combining Machine Learning and Fundamental Models. Inter-American Development Bank, 2022. http://dx.doi.org/10.18235/0004491.

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The study of the predictability of exchange rates has been a very recurring theme on the economics literature for decades, and very often is not possible to beat a random walk prediction, particularly when trying to forecast short time periods. Although there are several studies about exchange rate forecasting in general, predictions of specifically Brazilian real (BRL) to United States dollar (USD) exchange rates are very hard to find in the literature. The objective of this work is to predict the specific BRL to USD exchange rates by applying machine learning models combined with fundamental
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Yu, Zizhong, Zhao Zhao, Xinyue Chen, et al. Effects of Standardized Patients (SP) Combined Case-Based Learning (CBL) in Chinese Clinical Education : a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2025. https://doi.org/10.37766/inplasy2025.6.0118.

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Goulet Coulombe, Philippe, Massimiliano Marcellino, and Dalibor Stevanovic. Panel Machine Learning with Mixed-Frequency Data: Monitoring State-Level Fiscal Variables. CIRANO, 2025. https://doi.org/10.54932/qgja3449.

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Abstract:
We study the nowcasting of U.S. state-level fiscal variables using machine learning (ML) models and mixed-frequency predictors within a panel framework. Neural networks with continuous and categorical embeddings consistently outperform both linear and nonlinear alternatives, especially when combined with pooled panel structures. These architectures flexibly capture differences across states while benefiting from shared patterns in the panel structure. Forecast gains are especially large for volatile variables like expenditures and deficits. Pooling enhances forecast stability, and ML models ar
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