Abstract
Previous chapters have discussed the use of decision forests in supervised problems as well as unsupervised ones. This chapter puts the two things together to achieve semi-supervised learning. We focus here on semi-supervised classification, but the approach can be extended to regression too. In semi-supervised classification we have available a small set of labeled training data points and a large set of unlabeled ones. This is a typical situation in many practical scenarios. For instance, in medical image analysis, getting hold of numerous anonymized patients scans is relatively easy and cheap. However, labeling them with ground truth annotations requires experts time and effort and thus it is very expensive. A key question then is whether we can exploit the existence of unlabeled data to improve classification. After a brief literature survey, we show how to adapt the abstract forest model of Chap. 3 to achieve efficient semi-supervised classification.
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Notes
- 1.
In this experiment the SVM and transductive SVM results were generated using the “SVM-light” Matlab toolbox in http://svmlight.joachims.org/. Parameters were chosen manually to try and produce the visually best results.
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Criminisi, A., Shotton, J. (2013). Semi-supervised Classification Forests. In: Criminisi, A., Shotton, J. (eds) Decision Forests for Computer Vision and Medical Image Analysis. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-4929-3_8
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DOI: https://doi.org/10.1007/978-1-4471-4929-3_8
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