SemiBoost : Boosting for Semi-supervised Learning. Pavan Kumar Mallapragada , Student Member, IEEE, Rong Jin, Member, IEEE, Anil K. Jain, Fellow, IEEE, and Yi Liu, Student Member, IEEE. Presented by Yueng-Tien ,Lo. Reference:
Pavan Kumar Mallapragada, Student Member, IEEE, Rong Jin, Member, IEEE,
Anil K. Jain, Fellow, IEEE, and Yi Liu, Student Member, IEEE
Presented by Yueng-Tien ,Lo
SemiBoost: Boosting for Semi-supervised Learning P. K. Mallapragada, R. Jin, A. K. Jain, and Y. Liu,
IEEE Transaction on Pattern Analysis and Machine Intelligence(PAMI), 31(11):2000-2014, 2009
where L is the combinatorial graph Laplacian.
for each unlabeled example (using existing ensemble,
and the pairwise similarity).
combine them with the labeled samples and
train a component classifier using the supervised
learning algorithm A.
classifier with an appropriate weight.
and due to the symmetry of S
where is the hyperbolic cosine function.
where is the combination weight.
which is very similar to the weighting factor of AdaBoost, differing only by a constant factor of 1/2 .
X. Zhu, Z. Ghahramani, and J. Lafferty, “Semi-supervised learning using gaussian fields and harmonic functions,” in Proc. 20t International Conference on Machine Learning, pp. 912–919, 2003