Results 231 to 240 of about 2,076,683 (280)

Transferred Feature Selection

2009 IEEE International Conference on Data Mining Workshops, 2009
Traditional feature selection algorithms require a large number of labeled training instances to find out the most informative subset of features. However, in many real-world applications, the labeled data are often difficult, expensive or time-consuming to obtain.
Wei Bi, Yuan Shi, Zhenzhong Lan
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Transfer Reinforcement Learning: Feature Transferability in Ship Collision Avoidance

Volume 3B: 49th Design Automation Conference (DAC), 2023
Abstract The integration of artificial intelligence into engineering work has become increasingly prevalent. Engineering work processes can be highly complex, and learning from scratch requires large computation resources. Transfer learning has emerged as a promising technique for improving learning efficiency by leveraging knowledge ...
Xinrui Wang, Yan Jin
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Hierarchical Energy-transfer Features

Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 2014
In the paper, we propose the novel and efficient object descriptors that are designed to describe the appearance of the objects. The descriptors are called as Hierarchical Energy-Transfer Features (HETF). The main idea behind HETF is that the shape of the objects can be described by the function of energy distribution.
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