التفاصيل البيبلوغرافية
| العنوان: |
Convergence and Consistency of Regularized Boosting With Weakly Dependent Observations. |
| المؤلفون: |
Lozano, Aurelie C., Kulkarni, Sanjeev R., Schapire, Robert E. |
| المصدر: |
IEEE Transactions on Information Theory; Jan2014, Vol. 60 Issue 1, p651-660, 10p |
| مصطلحات موضوعية: |
STOCHASTIC convergence, MATHEMATICAL regularization, BOOSTING algorithms, MATHEMATICAL sequences, MATHEMATICAL proofs, COST functions, RANDOM variables |
| مستخلص: |
This paper studies the statistical convergence and consistency of regularized boosting methods, where the samples need not be independent and identically distributed but can come from stationary weakly dependent sequences. Consistency is proven for the composite classifiers that result from a regularization achieved by restricting the 1-norm of the base classifiers' weights. The less restrictive nature of sampling considered here is manifested in the consistency result through a generalized condition on the growth of the regularization parameter. The weaker the sample dependence, the faster the regularization parameter is allowed to grow with increasing sample size. A consistency result is also provided for data-dependent choices of the regularization parameter. [ABSTRACT FROM PUBLISHER] |
|
Copyright of IEEE Transactions on Information Theory is the property of IEEE and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Complementary Index |
