References¶
The following list includes important references used for implementations within libForest:
Online Random Forests / Decision Trees:
A. Saffari, C Leistner, J. Santner, M.Godec. On-Line Random Forests. International Conference on Computer Vision Workshops, 2009.
Variable Importance:
G. Louppe, L. Wehenkel, A. Sutera, P. Geurts. Understanding Variable Importance in Forests of Randomized Trees. Advances in Neural Information Processing Systems, 2013.
G. Louppe. Understanding Random Forests. PhD thesis, Universite de Liege, Belgium, 2014.
Density Forests:
A. Criminisi, J. Shotton. Density Forests. In Decision Forests for Computer Vision and Medical Image Analysis, Springer, 2013.
Kullback-Leibler Divergence:
F. Perez-Cruz. Kullback-Leibler DIvergence Estimation of Continuous Distributions. International Symposium on Information Theory, 2008.
Kernel Density Estimation:
B. E. Hansen. Lecture Notes on Nonparametrics. University of Wisconsin, 2009.
P. B. Stark. Statistics 240 Lecture Notes, part 10: Density Estimation. University of California Berkeley, 2008.
M. C. Jones, J. S. Marron, S. J. Sheather. A Brief Survey of Bandwidth Selection for Density Estimation. Journal of the American Statistical Association, 91(433), 1996.
B. A. Turlach. Bandwidth Selection in Kernel Density Estimation: A Review. C.O.R.E. and Intitut de Statistique, Universite Catholique de Louvain, Belgium.
K-Means:
D. Arthur, S. Vassilvitskii. k-means++: The Advantages of Careful Seeding. Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 2007.
C. Elkan. Using the Triangle Inequality to Accelerate k-Means. International Conference on Machine Learning, 2003.
J. Han, M. Kamber, J.Pei. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers Inc. San Francisco, CA, 2005.
Original page: https://github.com/strands-project/semantic_segmentation/blob/master/src/backend/third-party/libforest/docs/references.md