Abstract
Semi-supervised learning methods attempt to improve the performance of a supervised or an unsupervised learning in the presence of side information. This side information can be in the form of unlabeled samples in the supervised case or pairwise constraints in the unsupervised case
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Li, JB., Chu, SC., Pan, JS. (2014). Kernel Semi-Supervised Learning-Based Face Recognition. In: Kernel Learning Algorithms for Face Recognition. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-0161-2_7
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DOI: https://doi.org/10.1007/978-1-4614-0161-2_7
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