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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 ...
Xibin Dong, Zhiwen Yu, Wenming Cao
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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 ...
Xibin Dong, Zhiwen Yu, Wenming Cao
exaly +2 more sources
Transfer Learning and Ensemble Learning
2020In this chapter, we start from transfer learning and introduce the relationship between different learners; we use ensemble learning to combine them together and hope to get a strong learner from a weak learner by changing the training dataset or adjusting parameters of networks. Our ultimate goal is to implement a robust and stable classifier.
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Semantic Explanations in Ensemble Learning
2019A combination method is an integral part of an ensemble classifier. Existing combination methods determine the combined prediction of a new instance by relying on the predictions made by the majority of base classifiers. This can result in incorrect combined predictions when the majority predict the incorrect class.
Md Zahidul Islam 0001 +4 more
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Ensemble deep learning: A review
Engineering Applications of Artificial Intelligence, 2022M A Ganaie, Minghui Hu, A K Malik
exaly