… line 2 implements the semisupervised learning of the MLR … uses both the labeled and
unlabeled samples. Line 10 computes the multinomial probabilities for the complete hyperspectral …

… with the sparse multinomial logistic regression (SMLR) clas sifier learned with the MLR via
variable … Our experimental results with a real hyperspectral image collected by the NASA Jet …

… semi-supervised classification with limited training samples [7–11]. The multinomial logistic
regression (MLR) [12] also shows high quality while dealing with ill-posed problems, with …

… In this paper, we use active learning to construct small training sets with … Semi-supervised
hyperspectral image segmentation using multinomial logistic regression with active learning…

… Recently, the multinomial logistic regression (MLR) algorithm has been proven to be effective
in … Du, “A novel tri-training Technique for semi-supervised classification of hyperspectral …

… unlabeled information, eg, by means of active learning. There has been some work in the …
Li, A. Plaza, and J. Bioucas-Dias, “A new semi-supervised algorithm for hyperspectral image …

… A multinomial logistic regression (MLR) is used to model the posterior class probability …
semi-supervised hyperspectral image segmentation algorithm in which the final segmentation …

… and semi-supervised hyperspectral image segmentation. We … in this work is the use of
multinomial logistic regression (MLR) … In this work, we exploit active learning principles to increase …

… , Bioucas-Dias, and Plaza Citation2010). Ten-fold cross-validation is used to optimize … in
the semi-supervised classification of hyperspectral imagery. In this article, we use three different …

… Li, J. Bioucas-Dias, and A. Plaza, “Hyperspectral image segmentation using a new Bayesian
approach with active learning,” IEEE Trans. Geosci. Remote Sens., 2011, DOI: 10.1109/…