- Decision trees and random forests have been used in our group for some years. It is good to have a review on its basic theory, limitation and recent developments.
The study includes, but not limited to, the definition of decision trees, binary trees, multi-decision trees, ensemble methods, bagging, boosting, random forest, and applications to object recognition and super-resolution.
- Some attention must be given to “Randomness” theory and random trees.
- Please point out the significance of similarity and confidence measures. Give further examples (outside the paper(s)) of similarity and confidence measures, and the ways to achieve high confidence decision with a number of weak classifiers.
- Randomness Theory (Random Forests)
- Confidence Measurement
- Appendix: Ensemble Learning
 W.C. Siu, X.F. Yang, L.W. Wang, J.J. Huang and Z.S. Liu, “Introduction to Random Tree and Random Forests for Fast Signal Processing and Object Recognition”, Chapter 1 of Learning Approaches in Signal Processing”, Pan Stanford Series on Digital Signal Processing, Vol.2, November 2018 (Edited by W.C. Siu, L.P. Chau, L. Wang and T. Tan), November 2018.
 Gall, J.; Lempitsky, V., “Class-Specific Hough Forests for Object Detection”, Computer Vision and Pattern Recognition, 2009. IEEE Conference on CVPR 2009. vol., no., pp.1022,1029, 20-25 June 2009
 Jun-Jie Huang and Wan-Chi Siu, “Learning Hierarchical Decision Trees for Single Image Super-Resolution”, Paper Accepted, to be published in IEEE Transactions on Circuits & System for Video Technology. (IEEE Early Access Articles, pp.1-14, DOI:10.1109/TCSVT.2015.2513661, Year: 2016, Issue: 99.)
 Jun-Jie Huang, Wan-Chi Siu and Tian-Rui Liu, “Fast Image Interpolation via Random Forests,” IEEE Transactions on Image Processing, vol. 24, no. 10, pp. 3232-3245, 2015.
 Wikipedia, “Decision Tree Learning”