Results 241 to 250 of about 3,973,078 (288)
Some of the next articles are maybe not open access.
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, Zhen-zhong Lan
openaire +2 more sources
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, Zhen-zhong Lan
openaire +2 more sources
Hierarchical Energy-transfer Features
Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 2014In 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.
Radovan Fusek +3 more
openaire +2 more sources
Feature subspace transfer for collaborative filtering
Neurocomputing, 2014Abstract The sparsity problem is a major bottleneck for the collaborative filtering. Recently, transfer learning methods are introduced in collaborative filtering to alleviate the sparsity problem which aim to use the shared knowledge in related domains to help improve the prediction performance.
Jing Wang 0049, Liangwen Ke
openaire +1 more source