Results 271 to 280 of about 155,735 (298)
Applying explainable artificial intelligence to interpret supervised ensemble learning models for robust credit card fraud detection. [PDF]
Sci RepAwad SS +3 more
europepmc +1 more source
Ensemble Learning to Categorize the Bethesda System of Reporting of Cervical Cytology.
J CytolDey P.
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Diagnosis of SLAP lesions on shoulder MRI using a 2.5D deep learning and ensemble learning framework. [PDF]
Front SurgWang H +5 more
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Deep Ensemble Learning to Detect Retinal Vascular Leakage on Ultrawide-Field Fundus Photographs of Patients With Uveitis. [PDF]
Transl Vis Sci TechnolKim J +8 more
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Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2018
Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and ...
Lior Rokach
exaly +2 more sources
Ensemble methods are considered the state‐of‐the art solution for many machine learning challenges. Such methods improve the predictive performance of a single model by training multiple models and combining their predictions. This paper introduce the concept of ensemble learning, reviews traditional, novel and state‐of‐the‐art ensemble methods and ...
Lior Rokach
exaly +2 more sources
Frontiers of Computer Science, 2019
Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure ...
Zhiwen Yu, Wenming Cao, Yifan Shi
exaly +2 more sources
Despite significant successes achieved in knowledge discovery, traditional machine learning methods may fail to obtain satisfactory performances when dealing with complex data, such as imbalanced, high-dimensional, noisy data, etc. The reason behind is that it is difficult for these methods to capture multiple characteristics and underlying structure ...
Zhiwen Yu, Wenming Cao, Yifan Shi
exaly +2 more sources