Name
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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2911172.2911176 | AI Matters |
DocType | Volume | Issue |
Journal | 2 | 3 |
Citations | PageRank | References |
0 | 0.34 | 1 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lilian Berton | 1 | 16 | 7.82 |
| Alneu de Andrade Lopes | 2 | 275 | 29.00 |