„supervised learning“

Suchergebnisse

2.377 Treffer

• Self-supervised learning advanced plant disease image classification with SimCLR
  Bunyang, Songpol ; Thedwichienchai, Natdanai ; Pintong, Krisna ; Lael, Nuj ; Kunaborimas, Wuthipoom ; Boonrat, Phawit ; Siriborvornratanakul, Thitirat
• The power of one clean qubit in supervised machine learning
  Karimi, Mahsa ; Javadi-Abhari, Ali ; Simon, Christoph ; Ghobadi, Roohollah
• A systematic review for class-imbalance in semi-supervised learning
  Oliveira, Willian Dihanster Gomes de ; Berton, Lilian
• Quantitative Classification and Prediction of Diagenetic Facies in Tight Gas Sandstone Reservoirs via Unsupervised and Supervised Machine Learning Models: Ledong Area, Yinggehai Basin
  Zhao, Xiaobo ; Chen, Xiaojun ; Chen, Wen ; Liu, Mingqian ; Yao, Yue ; Wang, Hua ; Zhang, Hui ; Yao, Guangqing
• A novel image feature based self-supervised learning model for effective quality inspection in additive manufacturing
  Lui, Chun Fai ; Maged, Ahmed ; Xie, Min
• Improving the classification of veterinary thoracic radiographs through inter-species and inter-pathology self-supervised pre-training of deep learning models
  Celniak, Weronika ; Wodziński, Marek ; Jurgas, Artur ; Burti, Silvia ; Zotti, Alessandro ; Atzori, Manfredo ; Müller, Henning ; Banzato, Tommaso
• CONTRASTIVE SELF-SUPERVISED DATA FUSION FOR SATELLITE IMAGERY
  Scheibenreif, L. ; Mommert, Michael ; Borth, D.
• Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review
  Andaur Navarro, Constanza L. ; Damen, Johanna A. A. ; Takada, Toshihiko ; Nijman, Steven W. J. ; Dhiman, Paula ; Ma, Jie ; Collins, Gary S. ; Bajpai, Ram ; Riley, Richard D. ; Moons, Karel G. M. ; Hooft, Lotty
• Supervised Learning of Synthetic Big Data for Li‐Ion Battery Degradation Diagnosis
  Mayilvahanan, Karthik S. ; Takeuchi, Kenneth J. ; Takeuchi, Esther S. ; Marschilok, Amy C. ; West, Alan C.
• Similarity contrastive estimation for image and video soft contrastive self-supervised learning
  Denize, Julien ; Rabarisoa, Jaonary ; Orcesi, Astrid ; Hérault, Romain
• Peak ground acceleration prediction using supervised machine learning algorithm for the seismically hazardous Kachchh rift zone, Gujarat, India
  Mandal, Prantik ; Mandal, Priyank
• Optimized supervised learning approach to predict Parkinson’s disease with minimal attributes using PPMI Datasets
  Kanagaraj, S. ; Hema, M. S. ; Guptha, M. Nageswara
• Self-supervised group meiosis contrastive learning for EEG-based emotion recognition
  Kan, Haoning ; Yu, Jiale ; Huang, Jiajin ; Liu, Zihe ; Wang, Heqian ; Zhou, Haiyan
• Adversarial representation teaching with perturbation-agnostic student-teacher structure for semi-supervised learning
  Park, Jae Hyeon ; Kim, Ju Hyun ; Ngo, Ba Hung ; Kwon, Jung Eun ; Cho, Sung In
• Markedly Enhanced Analysis of Mass Spectrometry Images Using Weakly Supervised Machine Learning
  Gardner, Wil ; Winkler, David A. ; Bamford, Sarah E. ; Muir, Benjamin W. ; Pigram, Paul J.
• LaRW: boosting open-set semi-supervised learning with label-guided re-weighting
  Ouyang, Jihong ; Mao, Dong ; Meng, Qingyi
• Filter-based feature selection in the context of evolutionary neural networks in supervised machine learning
  Tallón-Ballesteros, Antonio J. ; Riquelme, José C. ; Ruiz, Roberto
• Development of supervised machine learning model for prediction of TEG regeneration performance in natural gas dehydration
  Moghaddam, Amin Hedayati ; Sadeq, Abdellatif Mohammad
• Multivariate Techniques for Parameter Selection and Data Analysis Exemplified by a Study of Pyrethroid Neurotoxicity †
  Ford, Martyn G. ; Livingstone, D. J.
• Assessment of the regeneration of landslides areas using unsupervised and supervised methods and explainable machine learning models
  Arrogante-Funes, Patricia ; Bruzón, Adrián G. ; Álvarez-Ripado, Ariadna ; Arrogante-Funes, Fátima ; Martín-González, Fidel ; Novillo, Carlos J.