Semi-supervised Learning

Semi-supervised Learning

1. What is Semi-supervised Learning

1. Supervised Learning

   labeled data:({(x^r,hat{y}^r}_{r=1}^R)

   E.g: image,(hat{y}^r): class labeles

2. Unsupervised Learning

   unlabeled data:({x^r}_{r=1}^R)

   E.g: Clustering problem

3. Semi-supervised Learning

   both labeled data and unlabeled data:({(x^r,hat{y}^r)}_{r=1}^R,{x^u}_{u=R}^{R+U})

   • A set of unlabeled data,usually U >> R
   • Transductive Learning: unlabeled data is the testing data
   • Inductive Learning: unlabeled data is not the testing data

2. Why Semi-supervied Learning

• It's easy to collect unlabeled data
• We do semi-supervied learning in our lives