Artículos de revistas sobre el tema "Selected subset of training data"
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Liu, Xiao Fang y Chun Yang. "Training Data Reduction and Classification Based on Greedy Kernel Principal Component Analysis and Fuzzy C-Means Algorithm". Applied Mechanics and Materials 347-350 (agosto de 2013): 2390–94. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2390.
Texto completoYu, Siwei, Jianwei Ma y Stanley Osher. "Monte Carlo data-driven tight frame for seismic data recovery". GEOPHYSICS 81, n.º 4 (julio de 2016): V327—V340. http://dx.doi.org/10.1190/geo2015-0343.1.
Texto completoUkil, Arijit, Leandro Marin y Antonio J. Jara. "When less is more powerful: Shapley value attributed ablation with augmented learning for practical time series sensor data classification". PLOS ONE 17, n.º 11 (23 de noviembre de 2022): e0277975. http://dx.doi.org/10.1371/journal.pone.0277975.
Texto completoHampson, Daniel P., James S. Schuelke y John A. Quirein. "Use of multiattribute transforms to predict log properties from seismic data". GEOPHYSICS 66, n.º 1 (enero de 2001): 220–36. http://dx.doi.org/10.1190/1.1444899.
Texto completoAbuassba, Adnan O. M., Dezheng Zhang, Xiong Luo, Ahmad Shaheryar y Hazrat Ali. "Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines". Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3405463.
Texto completoLai, Feilin y Xiaojun Yang. "Improving Land Cover Classification Over a Large Coastal City Through Stacked Generalization with Filtered Training Samples". Photogrammetric Engineering & Remote Sensing 88, n.º 7 (1 de julio de 2022): 451–59. http://dx.doi.org/10.14358/pers.21-00035r3.
Texto completoHao, Ruqian, Lin Liu, Jing Zhang, Xiangzhou Wang, Juanxiu Liu, Xiaohui Du, Wen He, Jicheng Liao, Lu Liu y Yuanying Mao. "A Data-Efficient Framework for the Identification of Vaginitis Based on Deep Learning". Journal of Healthcare Engineering 2022 (27 de febrero de 2022): 1–11. http://dx.doi.org/10.1155/2022/1929371.
Texto completoYao, Yu Kai, Yang Liu, Zhao Li y Xiao Yun Chen. "An Effective K-Means Clustering Based SVM Algorithm". Applied Mechanics and Materials 333-335 (julio de 2013): 1344–48. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.1344.
Texto completoNakoneczny, S. J., M. Bilicki, A. Pollo, M. Asgari, A. Dvornik, T. Erben, B. Giblin et al. "Photometric selection and redshifts for quasars in the Kilo-Degree Survey Data Release 4". Astronomy & Astrophysics 649 (mayo de 2021): A81. http://dx.doi.org/10.1051/0004-6361/202039684.
Texto completoZavala, Valentina A., Tatiana Vidaurre, Xiaosong Huang, Sandro Casavilca, Jeannie Navarro, Michelle A. Williams, Sixto Sanchez et al. "Abstract 3683: Identification of optimal set of genetic variants from a previously reported polygenic risk score for breast cancer risk prediction in Latin American women". Cancer Research 82, n.º 12_Supplement (15 de junio de 2022): 3683. http://dx.doi.org/10.1158/1538-7445.am2022-3683.
Texto completoSwartz, James A., Qiao Lin y Yerim Kim. "A measurement invariance analysis of selected Opioid Overdose Knowledge Scale (OOKS) items among bystanders and first responders". PLOS ONE 17, n.º 10 (14 de octubre de 2022): e0271418. http://dx.doi.org/10.1371/journal.pone.0271418.
Texto completoChen, Yen-Liang, Li-Chen Cheng y Yi-Jun Zhang. "Building a training dataset for classification under a cost limitation". Electronic Library 39, n.º 1 (24 de febrero de 2021): 77–96. http://dx.doi.org/10.1108/el-07-2020-0209.
Texto completoJia, Jinyuan, Yupei Liu, Xiaoyu Cao y Neil Zhenqiang Gong. "Certified Robustness of Nearest Neighbors against Data Poisoning and Backdoor Attacks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 9 (28 de junio de 2022): 9575–83. http://dx.doi.org/10.1609/aaai.v36i9.21191.
Texto completoAhmad, Wasim, Sheraz Ali Khan, Cheol Hong Kim y Jong-Myon Kim. "Feature Selection for Improving Failure Detection in Hard Disk Drives Using a Genetic Algorithm and Significance Scores". Applied Sciences 10, n.º 9 (4 de mayo de 2020): 3200. http://dx.doi.org/10.3390/app10093200.
Texto completoCardellicchio, Angelo, Sergio Ruggieri, Valeria Leggieri y Giuseppina Uva. "View VULMA: Data Set for Training a Machine-Learning Tool for a Fast Vulnerability Analysis of Existing Buildings". Data 7, n.º 1 (31 de diciembre de 2021): 4. http://dx.doi.org/10.3390/data7010004.
Texto completoRen, Jiadong, Jiawei Guo, Wang Qian, Huang Yuan, Xiaobing Hao y Hu Jingjing. "Building an Effective Intrusion Detection System by Using Hybrid Data Optimization Based on Machine Learning Algorithms". Security and Communication Networks 2019 (16 de junio de 2019): 1–11. http://dx.doi.org/10.1155/2019/7130868.
Texto completoSitienei, Miriam, Ayubu Anapapa y Argwings Otieno. "Random Forest Regression in Maize Yield Prediction". Asian Journal of Probability and Statistics 23, n.º 4 (9 de agosto de 2023): 43–52. http://dx.doi.org/10.9734/ajpas/2023/v23i4511.
Texto completoXu, Xiaofeng, Ivor W. Tsang y Chuancai Liu. "Improving Generalization via Attribute Selection on Out-of-the-Box Data". Neural Computation 32, n.º 2 (febrero de 2020): 485–514. http://dx.doi.org/10.1162/neco_a_01256.
Texto completoAbuassba, Adnan Omer, Dezheng Zhang y Xiong Luo. "A Heterogeneous AdaBoost Ensemble Based Extreme Learning Machines for Imbalanced Data". International Journal of Cognitive Informatics and Natural Intelligence 13, n.º 3 (julio de 2019): 19–35. http://dx.doi.org/10.4018/ijcini.2019070102.
Texto completoZahedian, Sara, Przemysław Sekuła, Amir Nohekhan y Zachary Vander Laan. "Estimating Hourly Traffic Volumes using Artificial Neural Network with Additional Inputs from Automatic Traffic Recorders". Transportation Research Record: Journal of the Transportation Research Board 2674, n.º 3 (marzo de 2020): 272–82. http://dx.doi.org/10.1177/0361198120910737.
Texto completoDong, Naghedolfeizi, Aberra y Zeng. "Spectral–Spatial Discriminant Feature Learning for Hyperspectral Image Classification". Remote Sensing 11, n.º 13 (29 de junio de 2019): 1552. http://dx.doi.org/10.3390/rs11131552.
Texto completoGonzalez-Sanchez, Alberto, Juan Frausto-Solis y Waldo Ojeda-Bustamante. "Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction". Scientific World Journal 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/509429.
Texto completoNajafi-Ghiri, Mahdi, Marzieh Mokarram y Hamid Reza Owliaie. "Prediction of soil clay minerals from some soil properties with use of feature selection algorithm and ANFIS methods". Soil Research 57, n.º 7 (2019): 788. http://dx.doi.org/10.1071/sr18352.
Texto completoWoodrow, Sarah I., Mark Bernstein y M. Christopher Wallace. "Safety of intracranial aneurysm surgery performed in a postgraduate training program: implications for training". Journal of Neurosurgery 102, n.º 4 (abril de 2005): 616–21. http://dx.doi.org/10.3171/jns.2005.102.4.0616.
Texto completoSzyda, J., K. Żukowski, S. Kamiński y A. Żarnecki. "Testing different single nucleotide polymorphism selection strategies for prediction of genomic breeding values in dairy cattle based on low density panels". Czech Journal of Animal Science 58, No. 3 (4 de marzo de 2013): 136–45. http://dx.doi.org/10.17221/6670-cjas.
Texto completoHe, Ruimin, Xiaohua Yang, Tengxiang Li, Yaolin He, Xiaoxue Xie, Qilei Chen, Zijian Zhang y Tingting Cheng. "A Machine Learning-Based Predictive Model of Epidermal Growth Factor Mutations in Lung Adenocarcinomas". Cancers 14, n.º 19 (25 de septiembre de 2022): 4664. http://dx.doi.org/10.3390/cancers14194664.
Texto completoChau, K. W. y C. L. Wu. "A hybrid model coupled with singular spectrum analysis for daily rainfall prediction". Journal of Hydroinformatics 12, n.º 4 (2 de abril de 2010): 458–73. http://dx.doi.org/10.2166/hydro.2010.032.
Texto completoEl-Gawady, Aliaa, Mohamed A. Makhlouf, BenBella S. Tawfik y Hamed Nassar. "Machine Learning Framework for the Prediction of Alzheimer’s Disease Using Gene Expression Data Based on Efficient Gene Selection". Symmetry 14, n.º 3 (28 de febrero de 2022): 491. http://dx.doi.org/10.3390/sym14030491.
Texto completoMazloom, Reza, Hongmin Li, Doina Caragea, Cornelia Caragea y Muhammad Imran. "A Hybrid Domain Adaptation Approach for Identifying Crisis-Relevant Tweets". International Journal of Information Systems for Crisis Response and Management 11, n.º 2 (julio de 2019): 1–19. http://dx.doi.org/10.4018/ijiscram.2019070101.
Texto completoSharpe, P. K., H. E. Solberg, K. Rootwelt y M. Yearworth. "Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests". Clinical Chemistry 39, n.º 11 (1 de noviembre de 1993): 2248–53. http://dx.doi.org/10.1093/clinchem/39.11.2248.
Texto completoHensel, Stefan, Marin B. Marinov, Michael Koch y Dimitar Arnaudov. "Evaluation of Deep Learning-Based Neural Network Methods for Cloud Detection and Segmentation". Energies 14, n.º 19 (27 de septiembre de 2021): 6156. http://dx.doi.org/10.3390/en14196156.
Texto completoRamesh, Nisha, Ting Liu y Tolga Tasdizen. "Cell Detection Using Extremal Regions in a Semisupervised Learning Framework". Journal of Healthcare Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/4080874.
Texto completoYi, Liu, Diao Xing-chun, Cao Jian-jun, Zhou Xing y Shang Yu-ling. "A Method for Entity Resolution in High Dimensional Data Using Ensemble Classifiers". Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/4953280.
Texto completoMaya Gopal P S y Bhargavi R. "Selection of Important Features for Optimizing Crop Yield Prediction". International Journal of Agricultural and Environmental Information Systems 10, n.º 3 (julio de 2019): 54–71. http://dx.doi.org/10.4018/ijaeis.2019070104.
Texto completoLiu, Ruidan y Yu Dong. "Fault Diagnosis of Jointless Track Circuit Based on ReliefF-C4.5 Decision Tree". Journal of Physics: Conference Series 2383, n.º 1 (1 de diciembre de 2022): 012047. http://dx.doi.org/10.1088/1742-6596/2383/1/012047.
Texto completoAversa, Rossella, Piero Coronica, Cristiano De Nobili y Stefano Cozzini. "Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification". Data Intelligence 2, n.º 4 (octubre de 2020): 513–28. http://dx.doi.org/10.1162/dint_a_00062.
Texto completoA. Ramezan, Christopher, Timothy A. Warner y Aaron E. Maxwell. "Evaluation of Sampling and Cross-Validation Tuning Strategies for Regional-Scale Machine Learning Classification". Remote Sensing 11, n.º 2 (18 de enero de 2019): 185. http://dx.doi.org/10.3390/rs11020185.
Texto completoChatterjee, Soumick, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck y Andreas Nürnberger. "DS6: Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data". Journal of Imaging 8, n.º 10 (22 de septiembre de 2022): 259. http://dx.doi.org/10.3390/jimaging8100259.
Texto completoOglesby, Leslie W., Andrew R. Gallucci y Christopher J. Wynveen. "Athletic Trainer Burnout: A Systematic Review of the Literature". Journal of Athletic Training 55, n.º 4 (1 de abril de 2020): 416–30. http://dx.doi.org/10.4085/1062-6050-43-19.
Texto completoKutyłowska, M. "Forecasting failure rate of water pipes". Water Supply 19, n.º 1 (13 de abril de 2018): 264–73. http://dx.doi.org/10.2166/ws.2018.078.
Texto completoZhang, Ling, Zixuan Zhang, Zhaohui Xue y Hao Li. "Sensitive Feature Evaluation for Soil Moisture Retrieval Based on Multi-Source Remote Sensing Data with Few In-Situ Measurements: A Case Study of the Continental U.S." Water 13, n.º 15 (21 de julio de 2021): 2003. http://dx.doi.org/10.3390/w13152003.
Texto completoBraken, Rebecca, Alexander Paulus, André Pomp y Tobias Meisen. "An Evaluation of Link Prediction Approaches in Few-Shot Scenarios". Electronics 12, n.º 10 (19 de mayo de 2023): 2296. http://dx.doi.org/10.3390/electronics12102296.
Texto completoSolarz, A., R. Thomas, F. M. Montenegro-Montes, M. Gromadzki, E. Donoso, M. Koprowski, L. Wyrzykowski, C. G. Diaz, E. Sani y M. Bilicki. "Spectroscopic observations of the machine-learning selected anomaly catalogue from the AllWISE Sky Survey". Astronomy & Astrophysics 642 (octubre de 2020): A103. http://dx.doi.org/10.1051/0004-6361/202038439.
Texto completoJiang, Bingbing, Xingyu Wu, Kui Yu y Huanhuan Chen. "Joint Semi-Supervised Feature Selection and Classification through Bayesian Approach". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 3983–90. http://dx.doi.org/10.1609/aaai.v33i01.33013983.
Texto completoTurki, Turki, Zhi Wei y Jason T. L. Wang. "A transfer learning approach via procrustes analysis and mean shift for cancer drug sensitivity prediction". Journal of Bioinformatics and Computational Biology 16, n.º 03 (junio de 2018): 1840014. http://dx.doi.org/10.1142/s0219720018400140.
Texto completoZhang, Ying. "Real-Time Detection of Lower Limb Training Stability Function Based on Smart Wearable Sensors". Journal of Sensors 2022 (31 de julio de 2022): 1–12. http://dx.doi.org/10.1155/2022/7503668.
Texto completoHildebrand, J., S. Schulz, R. Richter y J. Döllner. "SIMULATING LIDAR TO CREATE TRAINING DATA FOR MACHINE LEARNING ON 3D POINT CLOUDS". ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences X-4/W2-2022 (14 de octubre de 2022): 105–12. http://dx.doi.org/10.5194/isprs-annals-x-4-w2-2022-105-2022.
Texto completoPyenson, Bruce, Maggie Alston, Jeffrey Gomberg, Feng Han, Nikhil Khandelwal, Motoharu Dei, Monica Son y Jaime Vora. "Applying Machine Learning Techniques to Identify Undiagnosed Patients with Exocrine Pancreatic Insufficiency". Journal of Health Economics and Outcomes Research 6, n.º 2 (14 de febrero de 2019): 32–46. http://dx.doi.org/10.36469/9727.
Texto completoPistoia, Jenny, Nadia Pinardi, Paolo Oddo, Matthew Collins, Gerasimos Korres y Yann Drillet. "Development of super-ensemble techniques for ocean analyses: the Mediterranean Sea case". Natural Hazards and Earth System Sciences 16, n.º 8 (9 de agosto de 2016): 1807–19. http://dx.doi.org/10.5194/nhess-16-1807-2016.
Texto completoMaya Gopal, P. S. y R. Bhargavi. "Optimum Feature Subset for Optimizing Crop Yield Prediction Using Filter and Wrapper Approaches". Applied Engineering in Agriculture 35, n.º 1 (2019): 9–14. http://dx.doi.org/10.13031/aea.12938.
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