Artículos de revistas sobre el tema "ENSEMBLE LEARNING MODELS"
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GURBYCH, A. "METHOD SUPER LEARNING FOR DETERMINATION OF MOLECULAR RELATIONSHIP". Herald of Khmelnytskyi National University. Technical sciences 307, n.º 2 (2 de mayo de 2022): 14–24. http://dx.doi.org/10.31891/2307-5732-2022-307-2-14-24.
Texto completoACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE y ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING". International Journal of Pattern Recognition and Artificial Intelligence 28, n.º 07 (14 de octubre de 2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.
Texto completoSiswoyo, Bambang, Zuraida Abal Abas, Ahmad Naim Che Pee, Rita Komalasari y Nano Suryana. "Ensemble machine learning algorithm optimization of bankruptcy prediction of bank". IAES International Journal of Artificial Intelligence (IJ-AI) 11, n.º 2 (1 de junio de 2022): 679. http://dx.doi.org/10.11591/ijai.v11.i2.pp679-686.
Texto completoHuang, Haifeng, Lei Huang, Rongjia Song, Feng Jiao y Tao Ai. "Bus Single-Trip Time Prediction Based on Ensemble Learning". Computational Intelligence and Neuroscience 2022 (11 de agosto de 2022): 1–24. http://dx.doi.org/10.1155/2022/6831167.
Texto completoRuaud, Albane, Niklas Pfister, Ruth E. Ley y Nicholas D. Youngblut. "Interpreting tree ensemble machine learning models with endoR". PLOS Computational Biology 18, n.º 12 (14 de diciembre de 2022): e1010714. http://dx.doi.org/10.1371/journal.pcbi.1010714.
Texto completoKhanna, Samarth y Kabir Nagpal. "Sign Language Interpretation using Ensembled Deep Learning Models". ITM Web of Conferences 53 (2023): 01003. http://dx.doi.org/10.1051/itmconf/20235301003.
Texto completoAlazba, Amal y Hamoud Aljamaan. "Software Defect Prediction Using Stacking Generalization of Optimized Tree-Based Ensembles". Applied Sciences 12, n.º 9 (30 de abril de 2022): 4577. http://dx.doi.org/10.3390/app12094577.
Texto completoSonawane, Deepkanchan Nanasaheb. "Ensemble Learning For Increasing Accuracy Data Models". IOSR Journal of Computer Engineering 9, n.º 1 (2013): 35–37. http://dx.doi.org/10.9790/0661-0913537.
Texto completoLi, Ziyue, Kan Ren, Yifan Yang, Xinyang Jiang, Yuqing Yang y Dongsheng Li. "Towards Inference Efficient Deep Ensemble Learning". Proceedings of the AAAI Conference on Artificial Intelligence 37, n.º 7 (26 de junio de 2023): 8711–19. http://dx.doi.org/10.1609/aaai.v37i7.26048.
Texto completoAbdillah, Abid Famasya, Cornelius Bagus Purnama Putra, Apriantoni Apriantoni, Safitri Juanita y Diana Purwitasari. "Ensemble-based Methods for Multi-label Classification on Biomedical Question-Answer Data". Journal of Information Systems Engineering and Business Intelligence 8, n.º 1 (26 de abril de 2022): 42–50. http://dx.doi.org/10.20473/jisebi.8.1.42-50.
Texto completoSaphal, Rohan, Balaraman Ravindran, Dheevatsa Mudigere, Sasikanth Avancha y Bharat Kaul. "ERLP: Ensembles of Reinforcement Learning Policies (Student Abstract)". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 10 (3 de abril de 2020): 13905–6. http://dx.doi.org/10.1609/aaai.v34i10.7225.
Texto completoQutub, Aseel, Asmaa Al-Mehmadi, Munirah Al-Hssan, Ruyan Aljohani y Hanan S. Alghamdi. "Prediction of Employee Attrition Using Machine Learning and Ensemble Methods". International Journal of Machine Learning and Computing 11, n.º 2 (marzo de 2021): 110–14. http://dx.doi.org/10.18178/ijmlc.2021.11.2.1022.
Texto completoQuartulli, Marco, Amaia Gil, Ane Miren Florez-Tapia, Pablo Cereijo, Elixabete Ayerbe y Igor G. Olaizola. "Ensemble Surrogate Models for Fast LIB Performance Predictions". Energies 14, n.º 14 (8 de julio de 2021): 4115. http://dx.doi.org/10.3390/en14144115.
Texto completoShen, Zhiqiang, Zhankui He y Xiangyang Xue. "MEAL: Multi-Model Ensemble via Adversarial Learning". Proceedings of the AAAI Conference on Artificial Intelligence 33 (17 de julio de 2019): 4886–93. http://dx.doi.org/10.1609/aaai.v33i01.33014886.
Texto completoStevens, Christophe AT, Alexander RM Lyons, Kanika I. Dharmayat, Alireza Mahani, Kausik K. Ray, Antonio J. Vallejo-Vaz y Mansour TA Sharabiani. "Ensemble machine learning methods in screening electronic health records: A scoping review". DIGITAL HEALTH 9 (enero de 2023): 205520762311732. http://dx.doi.org/10.1177/20552076231173225.
Texto completoChang-You Zhang, Chang-You Zhang, Jing-Jing Wang Chang-You Zhang, Li-Xia Wan Jing-Jing Wang y Ruo-Xue Yu Li-Xia Wan. "An Emotional Analysis Method Based on Multi Model Ensemble Learning". 電腦學刊 34, n.º 1 (febrero de 2023): 001–11. http://dx.doi.org/10.53106/199115992023023401001.
Texto completoDeore, Bhushan, Aditya Kyatham y Shubham Narkhede. "A novel approach to ensemble MLP and random forest for network security". ITM Web of Conferences 32 (2020): 03003. http://dx.doi.org/10.1051/itmconf/20203203003.
Texto completoKapil, Divya. "Enhancing MNIST Digit Recognition with Ensemble Learning Techniques". Mathematical Statistician and Engineering Applications 70, n.º 2 (26 de febrero de 2021): 1362–71. http://dx.doi.org/10.17762/msea.v70i2.2328.
Texto completoWu, Li-Ya y Sung-Shun Weng. "Ensemble Learning Models for Food Safety Risk Prediction". Sustainability 13, n.º 21 (7 de noviembre de 2021): 12291. http://dx.doi.org/10.3390/su132112291.
Texto completoCampos, David, Miao Zhang, Bin Yang, Tung Kieu, Chenjuan Guo y Christian S. Jensen. "LightTS: Lightweight Time Series Classification with Adaptive Ensemble Distillation". Proceedings of the ACM on Management of Data 1, n.º 2 (13 de junio de 2023): 1–27. http://dx.doi.org/10.1145/3589316.
Texto completoChopra, Anjali y Priyanka Bhilare. "Application of Ensemble Models in Credit Scoring Models". Business Perspectives and Research 6, n.º 2 (17 de abril de 2018): 129–41. http://dx.doi.org/10.1177/2278533718765531.
Texto completoKim, Yong-Woon, Yung-Cheol Byun y Addapalli V. N. Krishna. "Portrait Segmentation Using Ensemble of Heterogeneous Deep-Learning Models". Entropy 23, n.º 2 (5 de febrero de 2021): 197. http://dx.doi.org/10.3390/e23020197.
Texto completoKuo, Ming-Tse, Benny Wei-Yun Hsu, Yi Sheng Lin, Po-Chiung Fang, Hun-Ju Yu, Yu-Ting Hsiao y Vincent S. Tseng. "Deep Learning Approach in Image Diagnosis of Pseudomonas Keratitis". Diagnostics 12, n.º 12 (25 de noviembre de 2022): 2948. http://dx.doi.org/10.3390/diagnostics12122948.
Texto completoOner, Mahir y Alp Ustundag. "Combining predictive base models using deep ensemble learning". Journal of Intelligent & Fuzzy Systems 39, n.º 5 (19 de noviembre de 2020): 6657–68. http://dx.doi.org/10.3233/jifs-189126.
Texto completoNandhini, A. Sunitha, J. Balakrishna, R. Bala Manikandan y S. Bharath Kumar. "Advanced flood severity detection using ensemble learning models". Journal of Physics: Conference Series 1916, n.º 1 (1 de mayo de 2021): 012048. http://dx.doi.org/10.1088/1742-6596/1916/1/012048.
Texto completoHu, Pingfan, Zeren Jiao, Zhuoran Zhang y Qingsheng Wang. "Development of Solubility Prediction Models with Ensemble Learning". Industrial & Engineering Chemistry Research 60, n.º 30 (21 de julio de 2021): 11627–35. http://dx.doi.org/10.1021/acs.iecr.1c02142.
Texto completoLee, Junho, Wu Wang, Fouzi Harrou y Ying Sun. "Wind Power Prediction Using Ensemble Learning-Based Models". IEEE Access 8 (2020): 61517–27. http://dx.doi.org/10.1109/access.2020.2983234.
Texto completoAsadi, Nazanin, Abdolreza Mirzaei y Ehsan Haghshenas. "Multiple Observations HMM Learning by Aggregating Ensemble Models". IEEE Transactions on Signal Processing 61, n.º 22 (noviembre de 2013): 5767–76. http://dx.doi.org/10.1109/tsp.2013.2280179.
Texto completoLivieris, Ioannis E., Emmanuel Pintelas, Stavros Stavroyiannis y Panagiotis Pintelas. "Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series". Algorithms 13, n.º 5 (10 de mayo de 2020): 121. http://dx.doi.org/10.3390/a13050121.
Texto completoKarim, Zainoolabadien y Terence L. van Zyl. "Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion". Remote Sensing 13, n.º 21 (31 de octubre de 2021): 4394. http://dx.doi.org/10.3390/rs13214394.
Texto completoWang, Yiren, Lijun Wu, Yingce Xia, Tao Qin, ChengXiang Zhai y Tie-Yan Liu. "Transductive Ensemble Learning for Neural Machine Translation". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 04 (3 de abril de 2020): 6291–98. http://dx.doi.org/10.1609/aaai.v34i04.6097.
Texto completoGagne, David John, Amy McGovern y Ming Xue. "Machine Learning Enhancement of Storm-Scale Ensemble Probabilistic Quantitative Precipitation Forecasts". Weather and Forecasting 29, n.º 4 (22 de julio de 2014): 1024–43. http://dx.doi.org/10.1175/waf-d-13-00108.1.
Texto completoJoshi, Gaurav. "Implementation of Isotension Ensemble in Deep Learning". Turkish Journal of Computer and Mathematics Education (TURCOMAT) 9, n.º 2 (30 de diciembre de 2018): 576–86. http://dx.doi.org/10.17762/turcomat.v9i2.13861.
Texto completoStrobach, Ehud y Golan Bel. "Decadal Climate Predictions Using Sequential Learning Algorithms". Journal of Climate 29, n.º 10 (6 de mayo de 2016): 3787–809. http://dx.doi.org/10.1175/jcli-d-15-0648.1.
Texto completoNai-Arun, Nongyao y Punnee Sittidech. "Ensemble Learning Model for Diabetes Classification". Advanced Materials Research 931-932 (mayo de 2014): 1427–31. http://dx.doi.org/10.4028/www.scientific.net/amr.931-932.1427.
Texto completoNakata, Norio y Tsuyoshi Siina. "Ensemble Learning of Multiple Models Using Deep Learning for Multiclass Classification of Ultrasound Images of Hepatic Masses". Bioengineering 10, n.º 1 (5 de enero de 2023): 69. http://dx.doi.org/10.3390/bioengineering10010069.
Texto completoA, Prof Ajil, Tanvi Jain, T. M. Namratha, Vismaya S y Thummaluru Ganga Lakshmi. "Detection of PCOS using Ensemble Models". International Journal for Research in Applied Science and Engineering Technology 11, n.º 5 (31 de mayo de 2023): 420–25. http://dx.doi.org/10.22214/ijraset.2023.51426.
Texto completoKurilová, Veronika, Szabolcs Rajcsányi, Zuzana Rábeková, Jarmila Pavlovičová, Miloš Oravec y Nora Majtánová. "Detecting glaucoma from fundus images using ensemble learning". Journal of Electrical Engineering 74, n.º 4 (1 de agosto de 2023): 328–35. http://dx.doi.org/10.2478/jee-2023-0040.
Texto completoJaruskova, K. y S. Vallecorsa. "Ensemble Models for Calorimeter Simulations". Journal of Physics: Conference Series 2438, n.º 1 (1 de febrero de 2023): 012080. http://dx.doi.org/10.1088/1742-6596/2438/1/012080.
Texto completoHozhyi, O. P., O. O. Zhebko, I. O. Kalinina y T. A. Hannichenko. "Іntelligent classification system based on ensemble methods". System technologies 3, n.º 146 (11 de mayo de 2023): 61–75. http://dx.doi.org/10.34185/1562-9945-3-146-2023-07.
Texto completoFarias, G., E. Fabregas, I. Martínez, J. Vega, S. Dormido-Canto y H. Vargas. "Nuclear Fusion Pattern Recognition by Ensemble Learning". Complexity 2021 (29 de junio de 2021): 1–9. http://dx.doi.org/10.1155/2021/1207167.
Texto completoWhitaker, Tim y Darrell Whitley. "Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks". Proceedings of the AAAI Conference on Artificial Intelligence 36, n.º 8 (28 de junio de 2022): 8638–46. http://dx.doi.org/10.1609/aaai.v36i8.20842.
Texto completoSaleh, Hager, Sherif Mostafa, Lubna Abdelkareim Gabralla, Ahmad O. Aseeri y Shaker El-Sappagh. "Enhanced Arabic Sentiment Analysis Using a Novel Stacking Ensemble of Hybrid and Deep Learning Models". Applied Sciences 12, n.º 18 (7 de septiembre de 2022): 8967. http://dx.doi.org/10.3390/app12188967.
Texto completoBilotserkovskyy, V. V., S. G. Udovenko y L. E. Chala. "Method of neural network recognition of falsified images". Bionics of Intelligence 2, n.º 95 (2 de diciembre de 2020): 32–42. http://dx.doi.org/10.30837/bi.2020.2(95).05.
Texto completoThapa, Niraj, Zhipeng Liu, Addison Shaver, Albert Esterline, Balakrishna Gokaraju y Kaushik Roy. "Secure Cyber Defense: An Analysis of Network Intrusion-Based Dataset CCD-IDSv1 with Machine Learning and Deep Learning Models". Electronics 10, n.º 15 (21 de julio de 2021): 1747. http://dx.doi.org/10.3390/electronics10151747.
Texto completoNithin, V. Joe y Prof S. Pallam Setty. "Prediction of Diabetes Using Ensemble Learning". International Journal for Research in Applied Science and Engineering Technology 10, n.º 10 (31 de octubre de 2022): 932–35. http://dx.doi.org/10.22214/ijraset.2022.47114.
Texto completoIskanderani, Ahmed I., Ibrahim M. Mehedi, Abdulah Jeza Aljohani, Mohammad Shorfuzzaman, Farzana Akther, Thangam Palaniswamy, Shaikh Abdul Latif, Abdul Latif y Aftab Alam. "Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases". Journal of Healthcare Engineering 2021 (28 de mayo de 2021): 1–7. http://dx.doi.org/10.1155/2021/3277988.
Texto completoRajaraman, Sivaramakrishnan, Feng Yang, Ghada Zamzmi, Zhiyun Xue y Sameer K. Antani. "A Systematic Evaluation of Ensemble Learning Methods for Fine-Grained Semantic Segmentation of Tuberculosis-Consistent Lesions in Chest Radiographs". Bioengineering 9, n.º 9 (24 de agosto de 2022): 413. http://dx.doi.org/10.3390/bioengineering9090413.
Texto completoKo, Hyungjin, Jaewook Lee, Junyoung Byun, Bumho Son y Saerom Park. "Loss-Driven Adversarial Ensemble Deep Learning for On-Line Time Series Analysis". Sustainability 11, n.º 12 (25 de junio de 2019): 3489. http://dx.doi.org/10.3390/su11123489.
Texto completoKlaar, Anne Carolina Rodrigues, Stefano Frizzo Stefenon, Laio Oriel Seman, Viviana Cocco Mariani y Leandro dos Santos Coelho. "Structure Optimization of Ensemble Learning Methods and Seasonal Decomposition Approaches to Energy Price Forecasting in Latin America: A Case Study about Mexico". Energies 16, n.º 7 (31 de marzo de 2023): 3184. http://dx.doi.org/10.3390/en16073184.
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