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Artículos de revistas sobre el tema "Progressive learning"

Autor: Grafiati

Publicado: 4 de mayo de 2024

Última modificación: 26 de julio de 2025

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1

Acharya, Avidit, and Juan Ortner. "Progressive Learning." Econometrica 85, no. 6 (2017): 1965–90. http://dx.doi.org/10.3982/ecta14718.

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2

Yu, Zhengxu, Dong Shen, Zhongming Jin, Jianqiang Huang, Deng Cai, and Xian-Sheng Hua. "Progressive Transfer Learning." IEEE Transactions on Image Processing 31 (2022): 1340–48. http://dx.doi.org/10.1109/tip.2022.3141258.

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3

Daniels, Jonathan S., David Moreau, and Brooke N. Macnamara. "Learning and Transfer in Problem Solving Progressions." Journal of Intelligence 10, no. 4 (2022): 85. http://dx.doi.org/10.3390/jintelligence10040085.

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Do individuals learn more effectively when given progressive or variable problem-solving experience, relative to consistent problem-solving experience? We investigated this question using a Rubik’s Cube paradigm. Participants were randomly assigned to a progression-order condition, where they practiced solving three progressively more difficult Rubik’s Cubes (i.e., 2 × 2 × 2 to 3 × 3 × 3 to 4 × 4 × 4), a variable-order condition, where they practiced solving three Rubik’s Cubes of varying difficulty (e.g., 3 × 3 × 3 to 2 × 2 × 2 to 4 × 4 × 4), or a consistent-order condition, where they consis

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4

Yu, Zhiwen, Daxing Wang, Jane You, et al. "Progressive subspace ensemble learning." Pattern Recognition 60 (December 2016): 692–705. http://dx.doi.org/10.1016/j.patcog.2016.06.017.

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5

Sarutte, Atsawaraungsuk, Boonphairote Wasaya, Somsuk Kritsanapong, Suwannapong Chanwit, and Khummanee Suchart. "A progressive learning for structural tolerance online sequential extreme learning machine." TELKOMNIKA 21, no. 05 (2023): 1039–50. https://doi.org/10.12928/telkomnika.v21i5.24564.

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This article discusses the progressive learning for structural tolerance online sequential extreme learning machine (PSTOS-ELM). PSTOS-ELM can save robust accuracy while updating the new data and the new class data on the online training situation. The robustness accuracy arises from using the householder block exact QR decomposition recursive least squares (HBQRD-RLS) of the PSTOS-ELM. This method is suitable for applications that have data streaming and often have new class data. Our experiment compares the PSTOS-ELM accuracy and accuracy robustness while data is updating with the batch-extr

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6

Fayek, Haytham M., Lawrence Cavedon, and Hong Ren Wu. "Progressive learning: A deep learning framework for continual learning." Neural Networks 128 (August 2020): 345–57. http://dx.doi.org/10.1016/j.neunet.2020.05.011.

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7

Tribushinina, Elena, Angela Radić, and Gordana Hržica. "Bilingual effects in foreign language learning." Nota Bene 1, no. 2 (2024): 208–24. https://doi.org/10.1075/nb.00012.tri.

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Abstract This study tests the hypothesis that bilingualism is associated with advantages in learning foreign languages. We compare oral (narrative) English skills of 15 Dutch monolinguals (aged 10–12) and 15 age-matched bilinguals acquiring Dutch and Serbian/Croatian. We also test the hypothesis that bilinguals speaking an aspectual home language have an advantage over Dutch monolinguals in learning the English progressive aspect. Narratives produced by adult L1 English speakers were used to establish a baseline of aspectual choices. The results demonstrate that bilinguals outperformed monolin

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8

Ahmed, Naveed, JeeWoong Park, Cristian Arteaga, and Haroon Stephen. "Investigation of Progressive Learning within a Statics Course: An Analysis of Performance Retention, Critical Topics, and Active Participation." Education Sciences 13, no. 6 (2023): 576. http://dx.doi.org/10.3390/educsci13060576.

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Previous research has demonstrated a link between prior knowledge and student success in engineering courses. However, while course-to-course relations exist, researchers have paid insufficient attention to internal course performance development. This study aims to address this gap—designed to quantify and thus extract meaningful insights—by examining a fundamental engineering course, Statics, from three perspectives: (1) progressive learning reflected in performance retention throughout the course; (2) critical topics and their influence on students’ performance progression; and (3) student

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9

Boo-Ho Yang and H. Asada. "Progressive learning and its application to robot impedance learning." IEEE Transactions on Neural Networks 7, no. 4 (1996): 941–52. http://dx.doi.org/10.1109/72.508937.

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10

Gitlin, Andrew. "Global learning & progressive school change." Literacy Information and Computer Education Journal 6, no. 4 (2015): 2064–68. http://dx.doi.org/10.20533/licej.2040.2589.2015.0275.

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11

Zhang, Hui, Danqing Kang, Haibo He, and Fei-Yue Wang. "APLNet: Attention-enhanced progressive learning network." Neurocomputing 371 (January 2020): 166–76. http://dx.doi.org/10.1016/j.neucom.2019.08.086.

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12

Han, Bo, Ivor W. Tsang, Ling Chen, Celina P. Yu, and Sai-Fu Fung. "Progressive Stochastic Learning for Noisy Labels." IEEE Transactions on Neural Networks and Learning Systems 29, no. 10 (2018): 5136–48. http://dx.doi.org/10.1109/tnnls.2018.2792062.

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13

Yu, Zhiwen, Ye Lu, Jun Zhang, et al. "Progressive Semisupervised Learning of Multiple Classifiers." IEEE Transactions on Cybernetics 48, no. 2 (2018): 689–702. http://dx.doi.org/10.1109/tcyb.2017.2651114.

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14

Rama Chandra Charan, Sri. "Asynchronous Data Processing with Machine Learning: Progressive Web Applications for Seamless Offline Functionality." International Journal of Science and Research (IJSR) 11, no. 9 (2022): 1288–96. https://doi.org/10.21275/sr220912120313.

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15

Quan, Ruijie, Yu Wu, Xin Yu, and Yi Yang. "Progressive Transfer Learning for Face Anti-Spoofing." IEEE Transactions on Image Processing 30 (2021): 3946–55. http://dx.doi.org/10.1109/tip.2021.3066912.

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16

Corley, Gene. "Learning from disaster to prevent progressive collapse." Proceedings of the Institution of Civil Engineers - Civil Engineering 161, no. 6 (2008): 41–48. http://dx.doi.org/10.1680/cien.2008.161.6.41.

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17

Lv, Yang, and Chen Xi. "PET image reconstruction with deep progressive learning." Physics in Medicine & Biology 66, no. 10 (2021): 105016. http://dx.doi.org/10.1088/1361-6560/abfb17.

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18

Siddiqui, Zahid Ali, and Unsang Park. "Progressive Convolutional Neural Network for Incremental Learning." Electronics 10, no. 16 (2021): 1879. http://dx.doi.org/10.3390/electronics10161879.

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In this paper, we present a novel incremental learning technique to solve the catastrophic forgetting problem observed in the CNN architectures. We used a progressive deep neural network to incrementally learn new classes while keeping the performance of the network unchanged on old classes. The incremental training requires us to train the network only for new classes and fine-tune the final fully connected layer, without needing to train the entire network again, which significantly reduces the training time. We evaluate the proposed architecture extensively on image classification task usin

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19

Zhao, Haoran, Yanxian Bi, Shuwen Tian, et al. "Self-Knowledge Distillation via Progressive Associative Learning." Electronics 13, no. 11 (2024): 2062. http://dx.doi.org/10.3390/electronics13112062.

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As a specific form of knowledge distillation (KD), self-knowledge distillation enables a student network to progressively distill its own knowledge without relying on a pretrained, complex teacher network; however, recent studies of self-KD have discovered that additional dark knowledge captured by auxiliary architecture or data augmentation could create better soft targets for enhancing the network but at the cost of significantly more computations and/or parameters. Moreover, most existing self-KD methods extract the soft label as a supervisory signal from individual input samples, which ove

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20

Chen, Yisong, Guoping Wang, and Shihai Dong. "Learning with progressive transductive support vector machine." Pattern Recognition Letters 24, no. 12 (2003): 1845–55. http://dx.doi.org/10.1016/s0167-8655(03)00008-4.

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21

Barth, Alison L., and Ajit Ray. "Progressive Circuit Changes during Learning and Disease." Neuron 104, no. 1 (2019): 37–46. http://dx.doi.org/10.1016/j.neuron.2019.09.032.

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22

Zhang, Feiqian, Zhengxing Sun, Mofei Song, and Xufeng Lang. "Progressive 3D shape segmentation using online learning." Computer-Aided Design 58 (January 2015): 2–12. http://dx.doi.org/10.1016/j.cad.2014.08.008.

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23

Xu, Cai, Wei Zhao, Jinglong Zhao, et al. "Progressive Deep Multi-View Comprehensive Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 9 (2023): 10557–65. http://dx.doi.org/10.1609/aaai.v37i9.26254.

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Multi-view Comprehensive Representation Learning (MCRL) aims to synthesize information from multiple views to learn comprehensive representations of data items. Prevalent deep MCRL methods typically concatenate synergistic view-specific representations or average aligned view-specific representations in the fusion stage. However, the performance of synergistic fusion methods inevitably degenerate or even fail when partial views are missing in real-world applications; the aligned based fusion methods usually cannot fully exploit the complementarity of multi-view data. To eliminate all these dra

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24

Cárdenas, Monica, and Daniela Rocio Ramirez Orellana. "Progressive Reduction of Captions in Language Learning." Journal of Information Technology Education: Innovations in Practice 23 (2024): 002. http://dx.doi.org/10.28945/5263.

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Aim/Purpose: This exploratory qualitative case study examines the perceptions of high-school learners of English regarding a pedagogical intervention involving progressive reduction of captions (full, sentence-level, keyword captions, and no-captions) in enhancing language learning. Background: Recognizing the limitations of caption usage in fostering independent listening comprehension in non-captioned environments, this research builds upon and extends the foundational work of Vanderplank (2016), who highlighted the necessity of a comprehensive blend of tasks, strategies, focused viewing, an

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25

Barrat, Sabine, and Salvatore Tabbone. "A Progressive Learning Method for Symbol Recognition." JUCS - Journal of Universal Computer Science 14, no. (2) (2008): 224–36. https://doi.org/10.3217/jucs-014-02-0224.

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This paper deals with a progressive learning method for symbol recognition which improves its own recognition rate when new symbols are recognized in graphic documents. We propose a discriminant analysis method which provides allocation rules from a training set of labelled data. However a discriminant analysis method is efficient only if the training set and the test data are defined in the same conditions but it is rare in real life. In order to overcome this problem, a conditional vector is added to each instance to take into account the parasitic effects between the test data and the train

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26

Kimura, Daisuke, and Natalia Kazik. "Learning in-progress." Gesture 16, no. 1 (2017): 127–51. http://dx.doi.org/10.1075/gest.16.1.05kim.

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Abstract Though gesture is a growing area in second language research, its role in the teaching and learning of grammar remains on the margins. Drawing from Sociocultural Theory, the present case study addresses this gap by offering a microgenetic analysis of an ESL learner’s developing understanding of the progressive aspect. Our analysis is threefold. First, we observe how the learner’s gesture reveals her initial understanding of the progressive aspect. This is followed by study of the learner’s appropriation of the teacher’s gesture for the progressive aspect. Finally, we examine the cruci

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27

Rymarchuk, M. I. "DISTANCE LEARNING AS A PROGRESSIVE STEP ON THE POSTGRADUATE LEARNING PLATFORM." Bulletin of Problems Biology and Medicine 3, no. 152 (2019): 247. http://dx.doi.org/10.29254/2077-4214-2019-3-152-247-251.

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28

Clearihan, Lyn, Silvia Vogel, Leon Piterman, and Neil Spike. "Transgenerational learning: maximising resources, minimising teaching gaps and fostering progressive learning." Australian Journal of Primary Health 17, no. 1 (2011): 29. http://dx.doi.org/10.1071/py10057.

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The need to rationalise teaching resources underpinned a project at Monash University that used a Delphi technique to re-examine the teaching curriculum of two key topic areas in the medical curriculum – ophthalmology and dermatology – from an undergraduate, graduate and vocational perspective. Using Bloom’s taxonomy the learning objectives from these topic areas were collated and analysed. This process allowed the revising and redistributing of learning objectives of the curricula to reduce the likelihood of duplication of teaching or more importantly gaps in teaching occurring. This process

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29

Atsawaraungsuk, Sarutte, Wasaya Boonphairote, Kritsanapong Somsuk, Chanwit Suwannapong, and Suchart Khummanee. "A progressive learning for structural tolerance online sequential extreme learning machine." TELKOMNIKA (Telecommunication Computing Electronics and Control) 21, no. 5 (2023): 1039. http://dx.doi.org/10.12928/telkomnika.v21i5.24564.

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30

Hu, Wenyi, Yuchen Jin, Xuqing Wu, and Jiefu Chen. "Progressive transfer learning for low-frequency data prediction in full-waveform inversion." GEOPHYSICS 86, no. 4 (2021): R369—R382. http://dx.doi.org/10.1190/geo2020-0598.1.

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To effectively overcome the cycle-skipping issue in full-waveform inversion (FWI), we have developed a deep neural network (DNN) approach to predict the absent low-frequency (LF) components by exploiting the hidden physical relation connecting the LF and high-frequency (HF) data. To efficiently solve this challenging nonlinear regression problem, two novel strategies are proposed to design the DNN architecture and to optimize the learning process: (1) the dual data feed structure and (2) progressive transfer learning. With the dual data feed structure, not only the HF data, but also the corres

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31

Kapoor, Anikait, Debavushan Saikia, Prabhgun Kaur Khurana, and Ishaan Dhawan. "Smart Learning App For Coding." International Journal of Research in Science and Technology 15, no. 2 (2025): 7–18. https://doi.org/10.37648/ijrst.v15i02.002.

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With smart learning apps for encryption, progressive development of programming documents includes lessons in the field of programming documents. False Comfortable standard methods such as MOO and special data requirements, combining processes such as Intelligence (AI), code organization, and flexible learning frames. This is a high consideration for explaining the process and explaining the problem to solve the problem, and a discussion to criticize reality and AI proposals. This overview of various cases relating to how this experienced learning application is related to the obligation to cr

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32

Fahmi Aajami, Raghad. "A Cognitive Framework in Learning English Progressive Tense." International Journal of Language and Literary Studies 4, no. 2 (2022): 100–111. http://dx.doi.org/10.36892/ijlls.v4i2.924.

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Dealing with the English language and the skills of using it is still the focus of interest for many researchers, teachers and workers in the field of education. Cognitive grammar theory founded by Langacker (1987) is one of the prominent theories in this field. Iraqi students face a problem in mastering and understanding the use of the English language tenses. There are a lot of researches that have been conducted in the Iraqi context to improve the level of Iraqi students and to benefit from cognitive theory such as analysing the polysemy of English preposition. This research is an empirical

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33

Ma, Wenguang, Shibiao Xu, Wei Ma, Xiaopeng Zhang, and Hongbin Zha. "Progressive Feature Learning for Facade Parsing With Occlusions." IEEE Transactions on Image Processing 31 (2022): 2081–93. http://dx.doi.org/10.1109/tip.2022.3152004.

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34

Wang, Hui, Hanbin Zhao, and Xi Li. "Progressive Class-Based Expansion Learning for Image Classification." IEEE Signal Processing Letters 28 (2021): 1430–34. http://dx.doi.org/10.1109/lsp.2021.3094174.

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35

Sun, Fanghui, Shen Wang, and Hongli Zhang. "A progressive learning method on unknown protocol behaviors." Journal of Network and Computer Applications 197 (January 2022): 103249. http://dx.doi.org/10.1016/j.jnca.2021.103249.

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36

Duan, Yanfei, Yintian Liu, Ruixiang Wang, Dengguo Yao, and Hang Zhang. "Progressive face super-resolution via learning prior information." Journal of Physics: Conference Series 1651 (November 2020): 012127. http://dx.doi.org/10.1088/1742-6596/1651/1/012127.

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37

Warsito, D. Muhtadi, Sukirwan, and H. Saleh. "The Role of Progressive Mathematics in Geometry Learning." Journal of Physics: Conference Series 1613 (August 2020): 012042. http://dx.doi.org/10.1088/1742-6596/1613/1/012042.

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38

Moore, Philip B. "Research to Practice: Progressive Education and Accelerated Learning." Journal of Continuing Higher Education 54, no. 3 (2006): 48–51. http://dx.doi.org/10.1080/07377366.2006.10401225.

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39

Yan, Tiantian, Shijie Wang, Zhihui Wang, Haojie Li, and Zhongxuan Luo. "Progressive learning for weakly supervised fine-grained classification." Signal Processing 171 (June 2020): 107519. http://dx.doi.org/10.1016/j.sigpro.2020.107519.

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40

Yan, Yichao, Bingbing Ni, Huawei Wei, and Xiaokang Yang. "Fine-grained image analysis via progressive feature learning." Neurocomputing 396 (July 2020): 254–65. http://dx.doi.org/10.1016/j.neucom.2018.07.100.

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41

Samson, Kurt. "Study Suggests Learning Disabilities May Foretell Progressive Aphasia." Neurology Today 8, no. 6 (2008): 30–31. http://dx.doi.org/10.1097/01.nt.0000314565.66129.21.

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42

Maguire, Dee. "Progressive learning: structured induction for the novice nurse." British Journal of Nursing 22, no. 11 (2013): 645–49. http://dx.doi.org/10.12968/bjon.2013.22.11.645.

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43

de Carvalho, A., D. L. Bisset, and M. C. Fairhurst. "Progressive learning algorithm for GSN feedfoward neural architectures." Electronics Letters 30, no. 6 (1994): 506–7. http://dx.doi.org/10.1049/el:19940326.

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44

Lonka, Kirsti, Kai Hakkarainen, and Matti Sintonen. "Progressive inquiry learning for children — Experiences, possibilities, limitations." European Early Childhood Education Research Journal 8, no. 1 (2000): 7–23. http://dx.doi.org/10.1080/13502930085208461.

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45

Guo, Junwen, Guobao Xiao, Shiping Wang, and Jun Yu. "Graph Context Transformation Learning for Progressive Correspondence Pruning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 3 (2024): 1968–75. http://dx.doi.org/10.1609/aaai.v38i3.27967.

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Most of existing correspondence pruning methods only concentrate on gathering the context information as much as possible while neglecting effective ways to utilize such information. In order to tackle this dilemma, in this paper we propose Graph Context Transformation Network (GCT-Net) enhancing context information to conduct consensus guidance for progressive correspondence pruning. Specifically, we design the Graph Context Enhance Transformer which first generates the graph network and then transforms it into multi-branch graph contexts. Moreover, it employs self-attention and cross-attenti

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46

Liao, Dongping, Xitong Gao, Yabo Xu, and Cheng-Zhong Xu. "Progressive Distribution Matching for Federated Semi-Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5191–99. https://doi.org/10.1609/aaai.v39i5.32551.

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Federated Learning (FL) enables collaborative learning from distributed data while preserving the privacy of participating clients. While supervised federated learning with labeled data has made notable strides and achieved success, federated semi-supervised learning (FSSL) lags in its progress. Existing works for FSSL heavily rely on fully-labeled clients, while ignoring the distribution of pseudo-labels generated from skewed unlabeled data. In this work, we offer empirical and theoretical insights into the challenges encountered when applying conventional semi-supervised algorithms in the fe

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47

Budnick, D., A. Ghannoum, F. Steinlehner, et al. "Predicting Dynamic Process Limits in Progressive Die Sheet Metal Forming." IOP Conference Series: Materials Science and Engineering 1238, no. 1 (2022): 012068. http://dx.doi.org/10.1088/1757-899x/1238/1/012068.

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Abstract Tool makers have a limited selection of tools and are afforded limited flexibility during progressive die try-outs when attempting to identify suitable process control parameters and optimize throughput. The performance of a given tooling design hinges on selecting a suitable stroke rate for the press. Cost efficiencies are realized when operating a press at higher stroke rates, but risk subjecting the sheet metal strip to larger, uncontrolled oscillations, which can lead to collisions and strip-misalignment during strip progression. Introducing active control to the strip feeder and

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48

Navale, Girish Jaysing, Archana Bhise, and Kishor Wagh. "Progressive Brain Diseases: Advanced Diagnosis Using Machine Learning & Deep Learning Techniques." International Journal of Renewable Energy Exchange 12, no. 1 (2024): 105–11. http://dx.doi.org/10.58443/ijrex.12.1.2024.105-111.

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49

Jannah, Mawaddatul, and Rodli Al Anshori. "ARABIC LANGUAGE LEARNING IN THE MERDEKA CURRICULUM: A PROGRESSIVE PHILOSOPHY PERSPECTIVEARABIC LANGUAGE LEARNING IN THE MERDEKA CURRICULUM: A PROGRESSIVE PHILOSOPHY PERSPECTIVE." Kitaba 2, no. 2 (2024): 143–50. http://dx.doi.org/10.18860/kitaba.v2i2.26163.

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As times develop, the curriculum in Indonesia has changed from time to time until now it is called the Merdeka curriculum, which is a curriculum that gives students the freedom to explore learning. This study aims to determine the perspective of the progressivism school of philosophy on Arabic language learning in the Merdeka Curriculum. This research is a descriptive qualitative research using the library research method. Research data obtained from primary data sources and secondary data sources are then collected. The results of the data are presented using the deductive method from the gen

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50

Gariscsak, PJ, H. Braund, and F. Haji. "P.132 Investigation of Simulation-Based Lumbar Puncture Teaching Paradigms for Novice Learners." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 48, s3 (2021): S57. http://dx.doi.org/10.1017/cjn.2021.408.

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Background: Simulation-based educations’ prevalence within clinical neuroscience is on the rise, however investigation into what environment is most conducive to optimizing learning performance is limited. We aimed to determine whether training a simple-to-complex (progressive) sequence would result in superior learning compared to complex-to-simple (mixed) or complex-only sequences. Methods: A three-arm, prospective, randomised experiment was conducted to determine the effects on novice learner LP performance and cognitive load during learning and a very complex simulated reality assessment t

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