Learning with Ensembles: How Over--tting Can Be Useful
By David S. Touretzky, M. Mozer, Michael E. Hasselmo. 1996
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
Citations in the corpus
- An Ensemble of Epoch-Wise Empirical Bayes for Few-Shot Learning (2019, methodology): “It is a strategy [26, 85] to use multiple algorithms to improve machine learning performance, and which is proved to be effective to reduce the problems related to overfitting [35, 68].”
- Ensembling neural networks: Many could be better than all (2002, background): “Neural network ensemble is a learning paradigm where a collection of a finite number of neural networks is trained for the same task [42].”
- Popular Ensemble Methods: An Empirical Study (1999, background): “Previous work (Sollich & Krogh, 1996) has shown that optimizing the combining weights can lead to overfitting while an unweighted voting scheme is generally resilient to overfitting.”
- Neural Decision Trees (2017, background): “The motivation here is not necessarily to avoid overfitting, in fact, if using ensemble methods, over fitting is actually better to then leverage variance reduction from averaging Krogh (1996).”
- Hybrid and ensemble-based soft computing techniques in bankruptcy prediction: a survey (2010, background): “Krogh have shown that some over-fitting can be useful when designing an ensemble (Sollich and Krogh 1996).”
- Lung cancer cell identification based on artificial neural network ensembles (2002, background): “As to the definition of an artificial neural network ensemble, the most accepted one is that an artificial neural network ensemble is a collection of a (finite) number of artificial neural networks that are trained for the same task [27].”