Searching for "Stacked Regressions." – sorted by Relevance.
-
Dynamic Integration of Regression Models
- their performance to the base models and to the popular ensemble technique of Stacked Regression. We show
- Cited by 1 (0 self) – Add To MetaCart
-
Combining Grammars for Improved Learning
- for probabilistic context-free grammars (PCFGs). From stacked regression we borrow the basic idea of combining
- Add To MetaCart
-
Combining Estimates in Regression and Classification
- and studied it the regression setting, calling it "stacked regression". Here is how stacking works. We let c
- Cited by 61 (0 self) – Add To MetaCart
-
Stacked Generalization: when does it work?
- of stacked generalization to regression problems. The non-negativecoefficient least-squares algorithm
- Cited by 19 (0 self) – Add To MetaCart
-
Black-Box Software Sensor Design for Environmental Monitoring
- (jackknife samples) as output targets. Following stacking regression ideas [9], new potentially explicative
- Cited by 1 (1 self) – Add To MetaCart
-
For The Stock Market
- dummy for each firm (one stacked regression with 49 (76-K) observations) and (C) for the equally
- Add To MetaCart
-
A comparative assessment of classification methods
- stacked regressions method. Stacked regression is a method for forming a linear combination of different
- Cited by 2 (0 self) – Add To MetaCart
-
Weight Averaging for Neural Networks and Local Resampling Schemes
- )), Stacked Regression (Breiman, 1992) and Stacked Generalization (Wolpert, 1990). The weighting of estimators
- Cited by 3 (0 self) – Add To MetaCart
-
Constructive Algorithms for Hierarchical Mixtures of Experts
- obvious similarities with model merging techniques such as stacked regression (Wolpert 1993) in which
- Cited by 10 (2 self) – Add To MetaCart
-
Constructive Algorithms for Hierarchical Mixtures of Experts
- obvious similarities with model merging techniques such as stacked regression (Wolpert 1993) in which
- Add To MetaCart

