The weighted majority algorithm
Information and Computation
Making large-scale support vector machine learning practical
Advances in kernel methods
The impact of changing populations on classifier performance
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
On-line Algorithms in Machine Learning
Developments from a June 1996 seminar on Online algorithms: the state of the art
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
Classifier ensembles: Select real-world applications
Information Fusion
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Obtaining ground truth for hyperspectral data is an expensive task. In addition, a number of factors cause the spectral signatures of the same class to vary with location and/or time. Therefore, adapting a classifier designed from available labeled data to classify new hyperspectral images is difficult, but invaluable to the remote sensing community. In this paper, we use the Binary Hierarchical Classifier to propose a knowledge transfer framework that leverages the information gathered from existing labeled data to classify the data obtained from a spatially separate test area. Experimental results show that in the absence of any labeled data in the new area, our approach is better than a direct application of the old classifier on the new data. Moreover, when small amounts of labeled data are available from the new area, our framework offers further improvements through semi-supervised learning mechanisms.