Artículos de revistas sobre el tema "Limited training data"
Crea una cita precisa en los estilos APA, MLA, Chicago, Harvard y otros
Consulte los 50 mejores artículos de revistas para su investigación sobre el tema "Limited training data".
Junto a cada fuente en la lista de referencias hay un botón "Agregar a la bibliografía". Pulsa este botón, y generaremos automáticamente la referencia bibliográfica para la obra elegida en el estilo de cita que necesites: APA, MLA, Harvard, Vancouver, Chicago, etc.
También puede descargar el texto completo de la publicación académica en formato pdf y leer en línea su resumen siempre que esté disponible en los metadatos.
Explore artículos de revistas sobre una amplia variedad de disciplinas y organice su bibliografía correctamente.
Oh, Se Eun, Nate Mathews, Mohammad Saidur Rahman, Matthew Wright y Nicholas Hopper. "GANDaLF: GAN for Data-Limited Fingerprinting". Proceedings on Privacy Enhancing Technologies 2021, n.º 2 (29 de enero de 2021): 305–22. http://dx.doi.org/10.2478/popets-2021-0029.
Texto completoMcLaughlin, Niall, Ji Ming y Danny Crookes. "Robust Multimodal Person Identification With Limited Training Data". IEEE Transactions on Human-Machine Systems 43, n.º 2 (marzo de 2013): 214–24. http://dx.doi.org/10.1109/tsmcc.2012.2227959.
Texto completoZhang, Mingyang, Berrak Sisman, Li Zhao y Haizhou Li. "DeepConversion: Voice conversion with limited parallel training data". Speech Communication 122 (septiembre de 2020): 31–43. http://dx.doi.org/10.1016/j.specom.2020.05.004.
Texto completoQian, Tieyun, Bing Liu, Li Chen, Zhiyong Peng, Ming Zhong, Guoliang He, Xuhui Li y Gang Xu. "Tri-Training for authorship attribution with limited training data: a comprehensive study". Neurocomputing 171 (enero de 2016): 798–806. http://dx.doi.org/10.1016/j.neucom.2015.07.064.
Texto completoSaunders, Sara L., Ethan Leng, Benjamin Spilseth, Neil Wasserman, Gregory J. Metzger y Patrick J. Bolan. "Training Convolutional Networks for Prostate Segmentation With Limited Data". IEEE Access 9 (2021): 109214–23. http://dx.doi.org/10.1109/access.2021.3100585.
Texto completoZhao, Yao, Dong Joo Rhee, Carlos Cardenas, Laurence E. Court y Jinzhong Yang. "Training deep‐learning segmentation models from severely limited data". Medical Physics 48, n.º 4 (19 de febrero de 2021): 1697–706. http://dx.doi.org/10.1002/mp.14728.
Texto completoHoffbeck, J. P. y D. A. Landgrebe. "Covariance matrix estimation and classification with limited training data". IEEE Transactions on Pattern Analysis and Machine Intelligence 18, n.º 7 (julio de 1996): 763–67. http://dx.doi.org/10.1109/34.506799.
Texto completoCui, Kaiwen, Jiaxing Huang, Zhipeng Luo, Gongjie Zhang, Fangneng Zhan y Shijian Lu. "GenCo: Generative Co-training for Generative Adversarial Networks with Limited Data". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 1 (28 de junio de 2022): 499–507. http://dx.doi.org/10.1609/aaai.v36i1.19928.
Texto completoKim, June-Woo y Ho-Young Jung. "End-to-end speech recognition models using limited training data*". Phonetics and Speech Sciences 12, n.º 4 (diciembre de 2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.
Texto completoTambouratzis, George y Marina Vassiliou. "Swarm Algorithms for NLP - The Case of Limited Training Data". Journal of Artificial Intelligence and Soft Computing Research 9, n.º 3 (1 de julio de 2019): 219–34. http://dx.doi.org/10.2478/jaiscr-2019-0005.
Texto completoLiu, Weijian, Zhaojian Zhang, Jun Liu, Zheran Shang y Yong-Liang Wang. "Detection of a rank-one signal with limited training data". Signal Processing 186 (septiembre de 2021): 108120. http://dx.doi.org/10.1016/j.sigpro.2021.108120.
Texto completoPark, Ji-Hoon, Seung-Mo Seo y Ji-Hee Yoo. "SAR ATR for Limited Training Data Using DS-AE Network". Sensors 21, n.º 13 (1 de julio de 2021): 4538. http://dx.doi.org/10.3390/s21134538.
Texto completoKim, June-Woo y Ho-Young Jung. "End-to-end speech recognition models using limited training data*". Phonetics and Speech Sciences 12, n.º 4 (diciembre de 2020): 63–71. http://dx.doi.org/10.13064/ksss.2020.12.4.063.
Texto completoGhorbandoost, Mostafa, Abolghasem Sayadiyan, Mohsen Ahangar, Hamid Sheikhzadeh, Abdoreza Sabzi Shahrebabaki y Jamal Amini. "Voice conversion based on feature combination with limited training data". Speech Communication 67 (marzo de 2015): 113–28. http://dx.doi.org/10.1016/j.specom.2014.12.004.
Texto completoYAMASHITA, Masaru. "Acoustic HMMs to Detect Abnormal Respiration with Limited Training Data". IEICE Transactions on Information and Systems E106.D, n.º 3 (1 de marzo de 2023): 374–80. http://dx.doi.org/10.1587/transinf.2022edp7068.
Texto completoWang, Jingjing, Zheng Liu, Rong Xie y Lei Ran. "Radar HRRP Target Recognition Based on Dynamic Learning with Limited Training Data". Remote Sensing 13, n.º 4 (18 de febrero de 2021): 750. http://dx.doi.org/10.3390/rs13040750.
Texto completoXu, Ning, Yibing Tang, Jingyi Bao, Aiming Jiang, Xiaofeng Liu y Zhen Yang. "Voice conversion based on Gaussian processes by coherent and asymmetric training with limited training data". Speech Communication 58 (marzo de 2014): 124–38. http://dx.doi.org/10.1016/j.specom.2013.11.005.
Texto completoWang, S. L., A. W. C. Liew, W. H. Lau y S. H. Leung. "An Automatic Lipreading System for Spoken Digits With Limited Training Data". IEEE Transactions on Circuits and Systems for Video Technology 18, n.º 12 (diciembre de 2008): 1760–65. http://dx.doi.org/10.1109/tcsvt.2008.2004924.
Texto completoCreswell, Antonia, Alison Pouplin y Anil A. Bharath. "Denoising adversarial autoencoders: classifying skin lesions using limited labelled training data". IET Computer Vision 12, n.º 8 (12 de septiembre de 2018): 1105–11. http://dx.doi.org/10.1049/iet-cvi.2018.5243.
Texto completoAghamaleki, Javad Abbasi y Vahid Ashkani Chenarlogh. "Multi-stream CNN for facial expression recognition in limited training data". Multimedia Tools and Applications 78, n.º 16 (25 de abril de 2019): 22861–82. http://dx.doi.org/10.1007/s11042-019-7530-7.
Texto completoCrowson, Merry, Ron Hagensieker y Björn Waske. "Mapping land cover change in northern Brazil with limited training data". International Journal of Applied Earth Observation and Geoinformation 78 (junio de 2019): 202–14. http://dx.doi.org/10.1016/j.jag.2018.10.004.
Texto completoJannati, Mohammad Javad y Abolghasem Sayadiyan. "Part-Syllable Transformation-Based Voice Conversion with Very Limited Training Data". Circuits, Systems, and Signal Processing 37, n.º 5 (30 de agosto de 2017): 1935–57. http://dx.doi.org/10.1007/s00034-017-0639-x.
Texto completoDemir, Begum, Francesca Bovolo y Lorenzo Bruzzone. "Classification of Time Series of Multispectral Images With Limited Training Data". IEEE Transactions on Image Processing 22, n.º 8 (agosto de 2013): 3219–33. http://dx.doi.org/10.1109/tip.2013.2259838.
Texto completoLang, Yue, Qing Wang, Yang Yang, Chunping Hou, Yuan He y Jinchen Xu. "Person identification with limited training data using radar micro‐Doppler signatures". Microwave and Optical Technology Letters 62, n.º 3 (noviembre de 2019): 1060–68. http://dx.doi.org/10.1002/mop.32125.
Texto completoKhezri, Shirin, Jafar Tanha, Ali Ahmadi y Arash Sharifi. "STDS: self-training data streams for mining limited labeled data in non-stationary environment". Applied Intelligence 50, n.º 5 (21 de enero de 2020): 1448–67. http://dx.doi.org/10.1007/s10489-019-01585-3.
Texto completoTang, Yehui, Shan You, Chang Xu, Jin Han, Chen Qian, Boxin Shi, Chao Xu y Changshui Zhang. "Reborn Filters: Pruning Convolutional Neural Networks with Limited Data". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 5972–80. http://dx.doi.org/10.1609/aaai.v34i04.6058.
Texto completoDuong, Huu-Thanh, Tram-Anh Nguyen-Thi y Vinh Truong Hoang. "Vietnamese Sentiment Analysis under Limited Training Data Based on Deep Neural Networks". Complexity 2022 (30 de junio de 2022): 1–14. http://dx.doi.org/10.1155/2022/3188449.
Texto completoJackson, Q. y D. A. Landgrebe. "An adaptive classifier design for high-dimensional data analysis with a limited training data set". IEEE Transactions on Geoscience and Remote Sensing 39, n.º 12 (2001): 2664–79. http://dx.doi.org/10.1109/36.975001.
Texto completoChen, Shangyu, Wenya Wang y Sinno Jialin Pan. "Deep Neural Network Quantization via Layer-Wise Optimization Using Limited Training Data". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 3329–36. http://dx.doi.org/10.1609/aaai.v33i01.33013329.
Texto completoMamyrbayev, O. Zh, M. Othman, A. T. Akhmediyarova, A. S. Kydyrbekova y N. O. Mekebayev. "VOICE VERIFICATION USING I-VECTORS AND NEURAL NETWORKS WITH LIMITED TRAINING DATA". BULLETIN 3, n.º 379 (15 de junio de 2019): 36–43. http://dx.doi.org/10.32014/2019.2518-1467.66.
Texto completoZiv, J. "An efficient universal prediction algorithm for unknown sources with limited training data". IEEE Transactions on Information Theory 48, n.º 6 (junio de 2002): 1690–93. http://dx.doi.org/10.1109/tit.2002.1003847.
Texto completoOh, Yujin, Sangjoon Park y Jong Chul Ye. "Deep Learning COVID-19 Features on CXR Using Limited Training Data Sets". IEEE Transactions on Medical Imaging 39, n.º 8 (agosto de 2020): 2688–700. http://dx.doi.org/10.1109/tmi.2020.2993291.
Texto completoHou, Yuchao, Ting Xu, Hongping Hu, Peng Wang, Hongxin Xue y Yanping Bai. "MdpCaps-Csl for SAR Image Target Recognition With Limited Labeled Training Data". IEEE Access 8 (2020): 176217–31. http://dx.doi.org/10.1109/access.2020.3026469.
Texto completoGe, Zhiqiang, Zhihuan Song y Furong Gao. "Self-Training Statistical Quality Prediction of Batch Processes with Limited Quality Data". Industrial & Engineering Chemistry Research 52, n.º 2 (28 de diciembre de 2012): 979–84. http://dx.doi.org/10.1021/ie300616s.
Texto completoKaewtip, Kantapon, Abeer Alwan y Charles Taylor. "Robust Hidden Markov Models for limited training data for birdsong phrase classification". Journal of the Acoustical Society of America 141, n.º 5 (mayo de 2017): 3725–26. http://dx.doi.org/10.1121/1.4988171.
Texto completoKrishnagopal, Sanjukta, Yiannis Aloimonos y Michelle Girvan. "Similarity Learning and Generalization with Limited Data: A Reservoir Computing Approach". Complexity 2018 (1 de noviembre de 2018): 1–15. http://dx.doi.org/10.1155/2018/6953836.
Texto completoZiel, Florian. "Load Nowcasting: Predicting Actuals with Limited Data". Energies 13, n.º 6 (20 de marzo de 2020): 1443. http://dx.doi.org/10.3390/en13061443.
Texto completoSenchenkov, Valentin, Damir Absalyamov y Dmitriy Avsyukevich. "Diagnostics of life support systems with limited statistical data on failures". E3S Web of Conferences 140 (2019): 05002. http://dx.doi.org/10.1051/e3sconf/201914005002.
Texto completoBardis, Michelle, Roozbeh Houshyar, Chanon Chantaduly, Alexander Ushinsky, Justin Glavis-Bloom, Madeleine Shaver, Daniel Chow, Edward Uchio y Peter Chang. "Deep Learning with Limited Data: Organ Segmentation Performance by U-Net". Electronics 9, n.º 8 (26 de julio de 2020): 1199. http://dx.doi.org/10.3390/electronics9081199.
Texto completoHe, Qiuchen, Shaobo Li, Chuanjiang Li, Junxing Zhang, Ansi Zhang y Peng Zhou. "A Hybrid Matching Network for Fault Diagnosis under Different Working Conditions with Limited Data". Computational Intelligence and Neuroscience 2022 (1 de julio de 2022): 1–14. http://dx.doi.org/10.1155/2022/3024590.
Texto completoVidal, Joel, Guillem Vallicrosa, Robert Martí y Marc Barnada. "Brickognize: Applying Photo-Realistic Image Synthesis for Lego Bricks Recognition with Limited Data". Sensors 23, n.º 4 (8 de febrero de 2023): 1898. http://dx.doi.org/10.3390/s23041898.
Texto completoPark, Sangyong, Jaeseon Kim y Yong Seok Heo. "Semantic Segmentation Using Pixel-Wise Adaptive Label Smoothing via Self-Knowledge Distillation for Limited Labeling Data". Sensors 22, n.º 7 (29 de marzo de 2022): 2623. http://dx.doi.org/10.3390/s22072623.
Texto completoGimeno, Pablo, Victoria Mingote, Alfonso Ortega, Antonio Miguel y Eduardo Lleida. "Generalizing AUC Optimization to Multiclass Classification for Audio Segmentation With Limited Training Data". IEEE Signal Processing Letters 28 (2021): 1135–39. http://dx.doi.org/10.1109/lsp.2021.3084501.
Texto completoJafaryani, Mohamadreza, Hamid Sheikhzadeh y Vahid Pourahmadi. "Parallel voice conversion with limited training data using stochastic variational deep kernel learning". Engineering Applications of Artificial Intelligence 115 (octubre de 2022): 105279. http://dx.doi.org/10.1016/j.engappai.2022.105279.
Texto completoLi, Hai, Wenyu Song, Weijian Liu y Renbiao Wu. "Moving target detection with limited training data based on the subspace orthogonal projection". IET Radar, Sonar & Navigation 12, n.º 7 (julio de 2018): 679–84. http://dx.doi.org/10.1049/iet-rsn.2017.0449.
Texto completoZhang, Mengmeng, Wei Li, Ran Tao y Song Wang. "Transfer Learning for Optical and SAR Data Correspondence Identification With Limited Training Labels". IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (2021): 1545–57. http://dx.doi.org/10.1109/jstars.2020.3044643.
Texto completoDavari, Amirabbas, Erchan Aptoula, Berrin Yanikoglu, Andreas Maier y Christian Riess. "GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data". IEEE Geoscience and Remote Sensing Letters 15, n.º 6 (junio de 2018): 942–46. http://dx.doi.org/10.1109/lgrs.2018.2817361.
Texto completoSun, Yuanshuang, Yinghua Wang, Hongwei Liu, Ning Wang y Jian Wang. "SAR Target Recognition With Limited Training Data Based on Angular Rotation Generative Network". IEEE Geoscience and Remote Sensing Letters 17, n.º 11 (noviembre de 2020): 1928–32. http://dx.doi.org/10.1109/lgrs.2019.2958379.
Texto completoZeng, Dan, Luuk Spreeuwers, Raymond Veldhuis y Qijun Zhao. "Combined training strategy for low-resolution face recognition with limited application-specific data". IET Image Processing 13, n.º 10 (22 de agosto de 2019): 1790–96. http://dx.doi.org/10.1049/iet-ipr.2018.5732.
Texto completoShin, Hyunkyung, Hyeonung Shin, Wonje Choi, Jaesung Park, Minjae Park, Euiyul Koh y Honguk Woo. "Sample-Efficient Deep Learning Techniques for Burn Severity Assessment with Limited Data Conditions". Applied Sciences 12, n.º 14 (21 de julio de 2022): 7317. http://dx.doi.org/10.3390/app12147317.
Texto completo