Artykuły w czasopismach na temat „Benign overfitting”
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Bartlett, Peter L., Philip M. Long, Gábor Lugosi i Alexander Tsigler. "Benign overfitting in linear regression". Proceedings of the National Academy of Sciences 117, nr 48 (24.04.2020): 30063–70. http://dx.doi.org/10.1073/pnas.1907378117.
Pełny tekst źródłaPeters, Evan, i Maria Schuld. "Generalization despite overfitting in quantum machine learning models". Quantum 7 (20.12.2023): 1210. http://dx.doi.org/10.22331/q-2023-12-20-1210.
Pełny tekst źródłaBartlett, Peter L., Andrea Montanari i Alexander Rakhlin. "Deep learning: a statistical viewpoint". Acta Numerica 30 (maj 2021): 87–201. http://dx.doi.org/10.1017/s0962492921000027.
Pełny tekst źródłaWang, Ke, i Christos Thrampoulidis. "Binary Classification of Gaussian Mixtures: Abundance of Support Vectors, Benign Overfitting, and Regularization". SIAM Journal on Mathematics of Data Science 4, nr 1 (marzec 2022): 260–84. http://dx.doi.org/10.1137/21m1415121.
Pełny tekst źródłaHu, Wei. "Understanding Surprising Generalization Phenomena in Deep Learning". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 20 (24.03.2024): 22669. http://dx.doi.org/10.1609/aaai.v38i20.30285.
Pełny tekst źródłaMontaha, Sidratul, Sami Azam, A. K. M. Rakibul Haque Rafid, Sayma Islam, Pronab Ghosh i Mirjam Jonkman. "A shallow deep learning approach to classify skin cancer using down-scaling method to minimize time and space complexity". PLOS ONE 17, nr 8 (4.08.2022): e0269826. http://dx.doi.org/10.1371/journal.pone.0269826.
Pełny tekst źródłaWindisch, Paul, Carole Koechli, Susanne Rogers, Christina Schröder, Robert Förster, Daniel R. Zwahlen i Stephan Bodis. "Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review". Cancers 14, nr 11 (27.05.2022): 2676. http://dx.doi.org/10.3390/cancers14112676.
Pełny tekst źródłaLiang, ShuFen, HuiLin Liu, FangChen Yang, Chuanbo Qin i Yue Feng. "Classification of Benign and Malignant Pulmonary Nodules Using a Regularized Extreme Learning Machine". Journal of Medical Imaging and Health Informatics 11, nr 8 (1.08.2021): 2117–23. http://dx.doi.org/10.1166/jmihi.2021.3448.
Pełny tekst źródłaLiu, Xinwei, Xiaojun Jia, Jindong Gu, Yuan Xun, Siyuan Liang i Xiaochun Cao. "Does Few-Shot Learning Suffer from Backdoor Attacks?" Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 18 (24.03.2024): 19893–901. http://dx.doi.org/10.1609/aaai.v38i18.29965.
Pełny tekst źródłaDoimo, Diego, Aldo Glielmo, Sebastian Goldt i Alessandro Laio. "Redundant representations help generalization in wide neural networks * , †". Journal of Statistical Mechanics: Theory and Experiment 2023, nr 11 (1.11.2023): 114011. http://dx.doi.org/10.1088/1742-5468/aceb4f.
Pełny tekst źródłaLi, Jian, Yong Liu i Weiping Wang. "High-Dimensional Analysis for Generalized Nonlinear Regression: From Asymptotics to Algorithm". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 12 (24.03.2024): 13500–13508. http://dx.doi.org/10.1609/aaai.v38i12.29253.
Pełny tekst źródłaAlkhaleefah, Mohammad, Shang-Chih Ma, Yang-Lang Chang, Bormin Huang, Praveen Kumar Chittem i Vishnu Priya Achhannagari. "Double-Shot Transfer Learning for Breast Cancer Classification from X-Ray Images". Applied Sciences 10, nr 11 (9.06.2020): 3999. http://dx.doi.org/10.3390/app10113999.
Pełny tekst źródłaIstighosah, Maie, Andi Sunyoto i Tonny Hidayat. "Breast Cancer Detection in Histopathology Images using ResNet101 Architecture". sinkron 8, nr 4 (1.10.2023): 2138–49. http://dx.doi.org/10.33395/sinkron.v8i4.12948.
Pełny tekst źródłaAnjum, Sunila, Imran Ahmed, Muhammad Asif, Hanan Aljuaid, Fahad Alturise, Yazeed Yasin Ghadi i Rashad Elhabob. "Lung Cancer Classification in Histopathology Images Using Multiresolution Efficient Nets". Computational Intelligence and Neuroscience 2023 (16.10.2023): 1–12. http://dx.doi.org/10.1155/2023/7282944.
Pełny tekst źródłaNadkarni, Swati, i Kevin Noronha. "Breast cancer detection using ensemble of convolutional neural networks". International Journal of Electrical and Computer Engineering (IJECE) 14, nr 1 (1.02.2024): 1041. http://dx.doi.org/10.11591/ijece.v14i1.pp1041-1047.
Pełny tekst źródłaRen, Cheng, i Shouming Hou. "A Hybrid Deep Learning Approach for Lung Nodule Classification". Frontiers in Computing and Intelligent Systems 8, nr 1 (10.05.2024): 6–12. http://dx.doi.org/10.54097/498fxm65.
Pełny tekst źródłaZi Wei, Yee, Marina Md-Arshad, Adlina Abdul Samad i Norafida Ithnin. "Comparing Malware Attack Detection using Machine Learning Techniques in IoT Network Traffic". International Journal of Innovative Computing 13, nr 1 (30.05.2023): 21–27. http://dx.doi.org/10.11113/ijic.v13n1.384.
Pełny tekst źródłaPalla, Tarun Ganesh, i Shahab Tayeb. "Intelligent Mirai Malware Detection for IoT Nodes". Electronics 10, nr 11 (24.05.2021): 1241. http://dx.doi.org/10.3390/electronics10111241.
Pełny tekst źródłaAlruwaili, Madallah, i Walaa Gouda. "Automated Breast Cancer Detection Models Based on Transfer Learning". Sensors 22, nr 3 (24.01.2022): 876. http://dx.doi.org/10.3390/s22030876.
Pełny tekst źródłaLiu, Yaning, Lin Han, Hexiang Wang i Bo Yin. "Classification of papillary thyroid carcinoma histological images based on deep learning". Journal of Intelligent & Fuzzy Systems 40, nr 6 (21.06.2021): 12011–21. http://dx.doi.org/10.3233/jifs-210100.
Pełny tekst źródłaUllah, Naeem, Ali Javed, Ali Alhazmi, Syed M. Hasnain, Ali Tahir i Rehan Ashraf. "TumorDetNet: A unified deep learning model for brain tumor detection and classification". PLOS ONE 18, nr 9 (27.09.2023): e0291200. http://dx.doi.org/10.1371/journal.pone.0291200.
Pełny tekst źródłaZawad, Syed, Ahsan Ali, Pin-Yu Chen, Ali Anwar, Yi Zhou, Nathalie Baracaldo, Yuan Tian i Feng Yan. "Curse or Redemption? How Data Heterogeneity Affects the Robustness of Federated Learning". Proceedings of the AAAI Conference on Artificial Intelligence 35, nr 12 (18.05.2021): 10807–14. http://dx.doi.org/10.1609/aaai.v35i12.17291.
Pełny tekst źródłaGonzalez-Cuautle, David, Aldo Hernandez-Suarez, Gabriel Sanchez-Perez, Linda Karina Toscano-Medina, Jose Portillo-Portillo, Jesus Olivares-Mercado, Hector Manuel Perez-Meana i Ana Lucila Sandoval-Orozco. "Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets". Applied Sciences 10, nr 3 (22.01.2020): 794. http://dx.doi.org/10.3390/app10030794.
Pełny tekst źródłaSalama, Wessam M., Moustafa H. Aly i Azza M. Elbagoury. "Lung Images Segmentation and Classification Based on Deep Learning: A New Automated CNN Approach". Journal of Physics: Conference Series 2128, nr 1 (1.12.2021): 012011. http://dx.doi.org/10.1088/1742-6596/2128/1/012011.
Pełny tekst źródłaBalasubramaniam, Sathiyabhama, Yuvarajan Velmurugan, Dhayanithi Jaganathan i Seshathiri Dhanasekaran. "A Modified LeNet CNN for Breast Cancer Diagnosis in Ultrasound Images". Diagnostics 13, nr 17 (24.08.2023): 2746. http://dx.doi.org/10.3390/diagnostics13172746.
Pełny tekst źródłaRadhi, Eman, i Mohammed Kamil. "An automatic segmentation of breast ultrasound images using U-Net model". Serbian Journal of Electrical Engineering 20, nr 2 (2023): 191–203. http://dx.doi.org/10.2298/sjee2302191r.
Pełny tekst źródłaKujdowicz, Monika, Dominika Januś, Anna Taczanowska-Niemczuk, Marek W. Lankosz i Dariusz Adamek. "Raman Spectroscopy as a Potential Adjunct of Thyroid Nodule Evaluation: A Systematic Review". International Journal of Molecular Sciences 24, nr 20 (13.10.2023): 15131. http://dx.doi.org/10.3390/ijms242015131.
Pełny tekst źródłaAlhussainan, Norah Fahd, Belgacem Ben Youssef i Mohamed Maher Ben Ismail. "A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7". Computation 12, nr 3 (1.03.2024): 44. http://dx.doi.org/10.3390/computation12030044.
Pełny tekst źródłaWang, Ruikui, Yuanfang Guo i Yunhong Wang. "AGS: Affordable and Generalizable Substitute Training for Transferable Adversarial Attack". Proceedings of the AAAI Conference on Artificial Intelligence 38, nr 6 (24.03.2024): 5553–62. http://dx.doi.org/10.1609/aaai.v38i6.28365.
Pełny tekst źródłaShah, Rajesh P., Heather M. Selby, Pritam Mukherjee, Shefali Verma, Peiyi Xie, Qinmei Xu, Millie Das, Sachin Malik, Olivier Gevaert i Sandy Napel. "Machine Learning Radiomics Model for Early Identification of Small-Cell Lung Cancer on Computed Tomography Scans". JCO Clinical Cancer Informatics, nr 5 (czerwiec 2021): 746–57. http://dx.doi.org/10.1200/cci.21.00021.
Pełny tekst źródłaAlzubaidi, Laith, Omran Al-Shamma, Mohammed A. Fadhel, Laith Farhan, Jinglan Zhang i Ye Duan. "Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model". Electronics 9, nr 3 (6.03.2020): 445. http://dx.doi.org/10.3390/electronics9030445.
Pełny tekst źródłaWildeboer, Rogier R., Christophe K. Mannaerts, Ruud J. G. van Sloun, Lars Budäus, Derya Tilki, Hessel Wijkstra, Georg Salomon i Massimo Mischi. "Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics". European Radiology 30, nr 2 (10.10.2019): 806–15. http://dx.doi.org/10.1007/s00330-019-06436-w.
Pełny tekst źródłaAtarsaikhan, Gantugs, Isabel Mogollon, Katja Välimäki, Tuomas Mirtti, Teijo Pellinen i Lassi Paavolainen. "Abstract 892: Pan-cancer tumor microenvironment profiling with multiplexed immunofluorescence microscopy and self-supervised learning". Cancer Research 84, nr 6_Supplement (22.03.2024): 892. http://dx.doi.org/10.1158/1538-7445.am2024-892.
Pełny tekst źródłaFeng, Liqi, Yaqin Zhao, Yichao Sun, Wenxuan Zhao i Jiaxi Tang. "Action Recognition Using a Spatial-Temporal Network for Wild Felines". Animals 11, nr 2 (12.02.2021): 485. http://dx.doi.org/10.3390/ani11020485.
Pełny tekst źródłaTran-Quoc, Kim, Lieu B. Nguyen, Van Hai Luong i H. Nguyen-Xuan. "Machine learning for predicting mechanical behavior of concrete beams with 3D printed TPMS". Vietnam Journal of Mechanics 44, nr 4 (31.12.2022): 538–84. http://dx.doi.org/10.15625/0866-7136/17999.
Pełny tekst źródłaWang, Ke, Vidya Muthukumar i Christos Thrampoulidis. "Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation". IEEE Transactions on Information Theory, 2023, 1. http://dx.doi.org/10.1109/tit.2023.3320098.
Pełny tekst źródłaZhou, Lijia, Frederic Koehler, Danica J. Sutherland i Nathan Srebro. "Optimistic Rates: A Unifying Theory for Interpolation Learningand Regularization in Linear Regression". ACM / IMS Journal of Data Science, 16.11.2023. http://dx.doi.org/10.1145/3594234.
Pełny tekst źródłaZufry, Hendra, i Agus Arip Munawar. "Near-Infrared Spectroscopy for Distinguishing Malignancy in Thyroid Nodules". Applied Spectroscopy, 19.02.2024. http://dx.doi.org/10.1177/00037028241232440.
Pełny tekst źródłaTo, Tyrell, Tongtong Lu, Julie M. Jorns, Mollie Patton, Taly Gilat Schmidt, Tina Yen, Bing Yu i Dong Hye Ye. "Deep learning classification of deep ultraviolet fluorescence images toward intra-operative margin assessment in breast cancer". Frontiers in Oncology 13 (16.06.2023). http://dx.doi.org/10.3389/fonc.2023.1179025.
Pełny tekst źródłaKim, Taehyun, Woonyoung Chang, Jeongyoun Ahn i Sungkyu Jung. "Double data piling: a high-dimensional solution for asymptotically perfect multi-category classification". Journal of the Korean Statistical Society, 3.04.2024. http://dx.doi.org/10.1007/s42952-024-00263-6.
Pełny tekst źródłaFeliciani, Giacomo, Francesco Serra, Enrico Menghi, Fabio Ferroni, Anna Sarnelli, Carlo Feo, Maria Chiara Zatelli, Maria Rosaria Ambrosio, Melchiore Giganti i Aldo Carnevale. "Radiomics in the characterization of lipid-poor adrenal adenomas at unenhanced CT: time to look beyond usual density metrics". European Radiology, 11.08.2023. http://dx.doi.org/10.1007/s00330-023-10090-8.
Pełny tekst źródłaGiraldo‐Roldan, Daniela, Erin Crespo Cordeiro Ribeiro, Anna Luiza Damaceno Araújo, Paulo Victor Mendes Penafort, Viviane Mariano da Silva, Jeconias Câmara, Hélder Antônio Rebelo Pontes i in. "Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas". Journal of Oral Pathology & Medicine, 15.09.2023. http://dx.doi.org/10.1111/jop.13481.
Pełny tekst źródłaJiménez-Gaona, Yuliana, María José Rodríguez-Alvarez, Líder Escudero, Carlos Sandoval i Vasudevan Lakshminarayanan. "Ultrasound breast images denoising using generative adversarial networks (GANs)". Intelligent Data Analysis, 31.01.2024, 1–18. http://dx.doi.org/10.3233/ida-230631.
Pełny tekst źródłaYang, Fan, Yujie Li, Xiaolu Li, Xiaoduo Yu, Yanfeng Zhao, Lin Li, Lizhi Xie i Meng Lin. "The utility of texture analysis based on quantitative synthetic magnetic resonance imaging in nasopharyngeal carcinoma: a preliminary study". BMC Medical Imaging 23, nr 1 (25.01.2023). http://dx.doi.org/10.1186/s12880-023-00968-w.
Pełny tekst źródłaMontaha, Sidratul, Sami Azam, Md Rahad Islam Bhuiyan, Sadia Sultana Chowa, Md Saddam Hossain Mukta i Mirjam Jonkman. "Malignancy pattern analysis of breast ultrasound images using clinical features and a graph convolutional network". DIGITAL HEALTH 10 (styczeń 2024). http://dx.doi.org/10.1177/20552076241251660.
Pełny tekst źródłaSinghal, Aneesh B., Oguzhan Kursun, Mehmet A. Topcuoglu, Joshua Fok, Bruce Barton i Susanne Muehlschlegel. "Abstract WP431: Distinguishing RCVS-associated Subarachnoid Hemorrhage From Cryptogenic and Aneurysmal Subarachnoid Hemorrhage". Stroke 44, suppl_1 (luty 2013). http://dx.doi.org/10.1161/str.44.suppl_1.awp431.
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