Difference between revisions of "10-601 SSL"
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=== Summary === | === Summary === | ||
| − | + | You should know: | |
| + | * What semi-supervised learning is - i.e., what the inputs and outputs are. | ||
| + | * How K-means and mixture-models can be extended to perform SSL. | ||
| + | * The difference between transductive and inductive semi-supervised learning. | ||
| + | * What graph-based SSL is. | ||
| + | * The definition/implementation of the harmonic function SSL method (variously called wvRN, HF, co-EM, ...) | ||
Revision as of 16:30, 24 October 2013
Slides
Readings
- This is not covered in Mitchell. An optional reading is an excellent short textbook by Jerry Zhu: [http://www.morganclaypool.com/doi/abs/10.2200/S00196ED1V01Y200906AIM006
Introduction to Semi-Supervised Learning Synthesis Lectures on Artificial Intelligence and Machine Learning], Chapters 1-3 + 5. This is a free PDF if you're on the CMU network.
Summary
You should know:
- What semi-supervised learning is - i.e., what the inputs and outputs are.
- How K-means and mixture-models can be extended to perform SSL.
- The difference between transductive and inductive semi-supervised learning.
- What graph-based SSL is.
- The definition/implementation of the harmonic function SSL method (variously called wvRN, HF, co-EM, ...)
