Difference between revisions of "10-601B Generalization and Overfitting: Sample Complexity Results for Supervised Classification"
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Latest revision as of 21:46, 21 February 2016
This a lecture used in the Syllabus for Machine Learning 10-601B in Spring 2016
Slides
Readings
- Mitchell Chapter 7
What you should remember
- Distributional Learning Formulation.
- Definition of sample complexity vs time complexity.
- How sample complexity grows with 1/epsilon, 1/delta, and |H|...
- in the noise free case.
- in the "agnostic" setting, where noise is present and the learner outputs the smallest error-rate hypothesis.