generalization error造句

例句与造句

1. The bias variance decomposition is one way to quantify generalization error.
2. That rule has associated probabilistic bounds on the generalization error.
3. Of particular prominence is the generalization error bound on boosting algorithms and support vector machines.
4. Overfitting occurs when a model fits the data in the training set well, while incurring larger generalization error.
5. This test sample allows us to approximate the expected error and as a result approximate a particular form of the generalization error.
6. It's difficult to find generalization error in a sentence. 用generalization error造句挺难的
7. In preliminary experimental results with noisy datasets, BrownBoost outperformed AdaBoost's generalization error; however, LogitBoost performed as well as BrownBoost.
8. There are several algorithms that identify noisy training examples and removing the suspected noisy training examples prior to training has decreased generalization error with statistical significance.
9. The notion of margin is important in several machine learning classification algorithms, as it can be used to bound the generalization error of the classifier.
10. It is noteworthy that working in a higher-dimensional feature space increases the generalization error of support vector machines, although given enough samples the algorithm still performs well.
11. The goal is that, with high probability ( the " probably " part ), the selected function will have low generalization error ( the " approximately correct " part ).
12. In turn, if the final classifier is learned from the non-noisy examples, the generalization error of the final classifier may be much better than if learned from noisy and non-noisy examples.
13. They prove that the pseudo-VC-dimension of this class is O ( nt \ ln ( nt ) ), which immediately translates to a bound on their generalization error and sample-complexity.
14. The "'bias variance decomposition "'is a way of analyzing a learning algorithm's expected generalization error with respect to a particular problem as a sum of three terms, the bias, variance, and a quantity called the " irreducible error ", resulting from noise in the problem itself.