Difference between revisions of "10-601 Bias-Variance"
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=== Readings === | === Readings === | ||
− | Bishop: Chap 1, 2 | + | *Bishop: Chap 1, 2 |
− | Mitchell: Chap 5, 6 | + | *Mitchell: Chap 5, 6 |
− | [http://www.cs.cmu.edu/~epxing/papers/Old_papers/feature.pdf Feature Selection in Microarray Analysis], E.P. Xing, in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, 2003. | + | *[http://www.cs.cmu.edu/~epxing/papers/Old_papers/feature.pdf Feature Selection in Microarray Analysis], E.P. Xing, in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, 2003. |
− | [http://ai.stanford.edu/~ang/papers/icml98-fs.pdf On Feature Selection: Learning with Exponentially many Irrelevant Features as Training Examples], Andrew Y. Ng. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998. | + | *[http://ai.stanford.edu/~ang/papers/icml98-fs.pdf On Feature Selection: Learning with Exponentially many Irrelevant Features as Training Examples], Andrew Y. Ng. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998. |
=== Take home message === | === Take home message === |
Revision as of 07:54, 9 October 2013
Slides
Readings
- Bishop: Chap 1, 2
- Mitchell: Chap 5, 6
- Feature Selection in Microarray Analysis, E.P. Xing, in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, 2003.
- On Feature Selection: Learning with Exponentially many Irrelevant Features as Training Examples, Andrew Y. Ng. In Proceedings of the Fifteenth International Conference on Machine Learning, 1998.
Take home message
- Overfitting
- kNN
- Regression
- Bias-variance decomposition
- Structural risk minimization
- The battle against overfitting
- Cross validation
- Regularization
- Feature selection