Active Sample Selection and Correction Propagation on a Gradually-Augmented Graph

no code implementations • CVPR 2015 • Hang Su, Zhaozheng Yin, Takeo Kanade, Seungil Huh

When data have a complex manifold structure or the characteristics of data evolve over time, it is unrealistic to expect a graph-based semi-supervised learning method to achieve flawless classification given a small number of initial annotations.

General Classification