Lilian Berton
Abstract
Semi-supervised learning (SSL) is useful when few labeled and plenty of unlabeled examples are available. This occurs in most of the cases due to labeled instances be difficult, expensive and time consuming to be obtained since human experts are required for the labeling task (Chapelle, Schlkopf, & Zien, 2010). The training set contain labeled data represented by XL = {(x1, y1) ... (x1, y1)}, and unlabelled data represented as XU = {xl+1 ... xl+u}. The total amount of training data is X = l ∪ u. The set of labeled examples is associated with the labels LY = {y1, ..., yl} where yi ε {1,..., c} and c is the number of classes. The purpose of SSL is to infer the missing labels YU = {yl+1,..., yn} corresponding to the unlabelled set XU.
- Berton, L., & de Andrade Lopes, A. (2014). Graph construction based on labeled instances for semi-supervised learning. In 22nd international conference on pattern recognition (pp. 2477--2482). doi: 10.1109/ICPR.2014.428 Google Scholar
Digital Library
- Berton, L., Valverde-Rebaza, J., & de Andrade Lopes, A. (2015). Link prediction in graph construction for supervised and semi-supervised learning. In International joint conference on neural networks (pp. 1--8).Google Scholar
- Chapelle, O., Schlkopf, B., & Zien, A. (2010). Semi-supervised learning (1st ed.). The MIT Press. Google ScholarDigital Library
- Jebara, T., Wang, J., & Chang, S.-F. (2009). Graph construction and b-matching for semi-supervised learning. In 26th annual international conference on machine learning (pp. 441--448). Google ScholarDigital Library
- Vega-Oliveros, D. A., Berton, L., Eberle, A., Lopes, A. d. A., & Zhao, L. (2014). Regular graph construction for semi-supervised learning. In Journal of physics: Conference series (Vol. 490, pp. 012022-1-012022-4).Google Scholar
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