Bagging predictors

Cited by (1,119)

Items 1,101–1,119 of 1,119

1. Cross-Diagnosis Structural Correlates of Autistic-Like Social Communication Differences
   Kushki, Azadeh, Cardy, Robyn E, Panahandeh, Sina, Malihi, Mahan, Hammill, Christopher, Brian, Jessica, Iaboni, Alana, Taylor, Margot J, Schachar, Russell, Crosbie, Jennifer et al. · 2021 · Cerebral Cortex
2. P300 event-related potential detection using one-dimensional convolutional capsule networks
   Liu, Xiang, Xie, Qingsheng, Lv, Jian, Huang, Haisong, Wang, Weixing · 2021 · Expert Systems with Applications
3. Selection of Noninvasive Features in Wrist-Based Wearable Sensors to Predict Blood Glucose Concentrations Using Machine Learning Algorithms
   Bogue-Jimenez, Brian, Huang, Xiaolei, Powell, Douglas, Doblas, Ana · 2022 · Sensors
4. Machine learning model to predict oncologic outcomes for drugs in randomized clinical trials
   Schperberg, Alexander V., Boichard, Amélie, Tsigelny, Igor F., Richard, Stéphane B., Kurzrock, Razelle · 2020 · International Journal of Cancer
5. Age-related differences in cerebral blood flow and cortical thickness with an application to age prediction
   MacDonald, M. Ethan, Williams, Rebecca J., Rajashekar, Deepthi, Stafford, Randall B., Hanganu, Alexadru, Sun, Hongfu, Berman, Avery J.L., McCreary, Cheryl R., Frayne, Richard, Forkert, Nils D. et al. · 2020 · Neurobiology of Aging
6. An ensemble learning strategy for panel time series forecasting of excess mortality during the COVID-19 pandemic
   Ashofteh, Afshin, Bravo, Jorge M., Ayuso, Mercedes · 2022 · Applied Soft Computing
7. Variable time delay estimation in continuous industrial processes
   Cattaldo, Marco, Ferrer, Alberto, Måge, Ingrid · 2024 · Chemometrics and Intelligent Laboratory Systems
8. QASC: A Dataset for Question Answering via Sentence Composition
   Khot, Tushar, Clark, Peter, Guerquin, Michal, Jansen, Peter, Sabharwal, Ashish · 2020 · Proceedings of the AAAI Conference on Artificial Intelligence
9. Artificial Neural Networks Classification of Patients with Parkinsonism based on Gait
   Fernandes, Carlos, Fonseca, Luis, Ferreira, Flora, Gago, Miguel, Costa, Lus, Sousa, Nuno, Ferreira, Carlos, Gama, Joao, Erlhagen, Wolfram, Bicho, Estela · 2018 · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
10. A soft sensor for simulating algal cell density based on dynamic response to environmental changes in a eutrophic shallow lake
    Rao, Wenxin, Qian, Xin, Fan, Yifan, Liu, Tong · 2023 · Science of The Total Environment
11. Counteracting the contemporaneous proliferation of digital forgeries and fake news
    FERREIRA, ALEXANDRE, CARVALHO, TIAGO, ANDALÓ, FERNANDA, ROCHA, ANDERSON · 2019 · Anais da Academia Brasileira de Ciências
12. Quantum computing and supervised machine learning
    Oneto, L., Ridella, S., Anguita, D. · 2017 · Quantum Inspired Computational Intelligence
13. Machine-learnt versus analytical models of TCP throughput
    El Khayat, I., Geurts, P., Leduc, G. · 2007 · Computer Networks
14. Exploiting Data and Human Knowledge for Predicting Wildlife Poaching
    Gurumurthy, Swaminathan, Yu, Lantao, Zhang, Chenyan, Jin, Yongchao, Li, Weiping, Zhang, Xiaodong, Fang, Fei · 2018 · Proceedings of the 1st ACM SIGCAS Conference on Computing and Sustainable Societies
15. Engineering the Neural Automatic Passenger Counter
    Jahn, Nico, Siebert, Michael · 2022 · Engineering Applications of Artificial Intelligence
16. Modeling health risks using neural network ensembles
    Smith, Brandon M., Criminisi, Antonio, Sorek, Noam, Harari, Yaar, Sood, Neeraj, Heymsfield, Steven B. · 2024 · PLOS ONE
17. Intelligence plays dice: Stochasticity is essential for machine learning
    Sabuncu, Mert R. · 2020 · arXiv
18. TIPS: A Prompt Engineering Framework for Code Classification and Generation on Resource-Constrained Systems
    Chen, Wei-Cheng, Chen, Shih-Yeh · 2026 · IEEE Access
19. Recursive self-improvement of AI research agents
    Srikanth, Dhruv, Zhao, Bingchen, Xu, Dixing, Wu, Yuxiang, Jiang, Zhengyao · 2026 · arXiv