4. SEMI-SUPERVISED LEARNING PROBLEMS
(1)
Learn from labeled data
Inductive
Learning
(2)
Apply learning on
unlabeled data to label
them
Transductive
Learning
(4)
Apply learning on
unseen unlabeled data
(3)
If confident in labeling,
then learn from
(1) and (2)
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12. GENERATIVE VS DISCRIMINATIVE MODELS
Conditional Probability,
to determine class
boundaries
Transductive SVM,
Graph-based
methods
Joint Probability P(x,y),
for any given y, we can
generate its x
EM Algorithm,
Self-learning
Cannot be used without considering P(x)
Difficult because P(x|y) are inadequate
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