Regularized Supervised Classification Algorithm: Difference between revisions
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* <B>See:</B> [[Regularized Least-Squares Classification Algorithm]], [[Tikhonov Regularization Algorithm]], [[Reproducing Kernel Hilbert Space]], [[Ill-Formed Problem]]. | * <B>See:</B> [[Regularized Least-Squares Classification Algorithm]], [[Tikhonov Regularization Algorithm]], [[Reproducing Kernel Hilbert Space]], [[Ill-Formed Problem]]. | ||
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Latest revision as of 17:51, 23 September 2021
A Regularized Supervised Classification Algorithm is a Supervised Classification Algorithm that is a Regularized Algorithm.
- Example(s):
- See: Regularized Least-Squares Classification Algorithm, Tikhonov Regularization Algorithm, Reproducing Kernel Hilbert Space, Ill-Formed Problem.
References
2009
- (Rifkin, 2009) ⇒ Ryan Rifkin. (2009). “Multiclass Classification.” In: MIT Course, 9.520: Statistical Learning Theory and Applications, Spring 2009.
- QUOTE: ... there’s a whole cottage industry in fancy, sophisticated methods for multiclass classification. To the best of my knowledge, choosing properly tuned regularization classifiers (RLSC, SVM) as your underlying binary classifiers and using one-vs-all (OVA) or all-vs-all (AVA) works as well as anything else you can do.
2004
- (Rifkin & Klatau, 2004) ⇒ Ryan Rifkin, and Aldebaro Klautau. (2004). “In Defense of One-Vs-All Classification.” In: The Journal of Machine Learning Research, 5.
- A broad class of classification (and regression) algorithms can be derived from the general approach of Tikhonov regularization.