Mar
27
SoloGen
Machine Learning-related surfings of Amir massoud Farahmand + other interesting things!
Mar
19
Arthur C. Clarke
“He never grew up; but he never stopped growing”We missed Arthur C. Clarke a few hours ago.Regularization in Kernel Learning
a paper by S. Mendelson and J. Neeman (2008).
I need to take a look at this paper because they stated their results when the regularization term is not the usual RKHS norm squared. I’m wondering if their results can exploit “sparsity”.
Mar
11
Rademacher Complexities and related stuff
A few sources that talk about Rademacher complexities (and other stuff):
- O. Bousquet, S. Boucheron, and G. Lugosi, “Introduction to Statistical Learning Theory,” .
- P. Bartlett, O. Bousquet, and S. Mendelson, “Local Rademacher Complexities,” The Annals of Statistics,” 2005.
- V. Koltchinskii and D. Panchenko, “Rademacher Processes and Bounding the Risk of Function Learning,” ?.
- P. Bartlett and S. Mendelson, “Rademacher and Gaussian Complexities: Risk Bounds and Structural Results,” JMLR 2002 (I need to check this paper, especially because it defines Gaussian complexity. Is there any generalization for arbitrary “noise” term (instead of Gaussian or boolean)? )
Mar
5
Life Goes on in Tehran
A photoblog about Tehran and Iran.
Yes! It is not immediately related to machine learning. (;
Mar
3
Some References for Cross-Validation
- B. Efron and G. Gong, “A Leisurely Look at the Bootstrap, the Jackkife, and Cross-Validation,” 1983.
- M. Kearns, “A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with the Consequences for the Training-Test Split,” 1997.
- P. Burman, E. Chow, and D. Nolan, “A Cross-Validatory Method for Dependent Data,” 1994
- B. Efron and R. Tibshirani, “Improvements on Cross-Validation: The 0.632+ Bootstrap Method,” 1997.
- G. Golub, M. Heath, and G. Wahba, “Generalized Cross-Validation as a Method for Choosing a Good Ridge Regression,” 1979.
- J. Shao, “Linear Model Selection by Cross-Validation,” 1993. Leave-one-out CV is not consistent! He suggests to use n_v/n —> 1 method while n —> \infty.
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