# Probability for label correctness in semi-supervised learning

I am aware of the existence of semi-supervised learning approaches, such as the Ladder Network, where only a subset of the data is labeled. Are there any methods or papers which consider correctness probabilities for the labels of that training data subset? That is, some labels may be correct with 100% probability, while others may have only 70% or 45% probability of being correct. Any links to papers or work in this direction are highly appreciated.

• Just adjust the loss function? For example, with a binary classifier a probability of $p$ in one class induces a probability of $1-p$ on the other. In a multiclass setting you have to decide how to apportion the complement (equally?). With regression losses it's just a multiplicative coefficient.
– Emre
Jun 9, 2017 at 16:24
• Would simply adjusting the target work? If the probability of correctness is 40%, you could assume that the other classes have in sum a probability of 60%. Hence your target would not be 1,0,0,0 but 0.4,0.2,0.2,0.2, for example. Jan 1, 2019 at 21:59