Breiman, L., Stacked regression, Machine Learning,vol. 24, pp. 49-64, 1996. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
First 50 documents Next 50
Adaptive Approximate Querying of Large Sparse Binary Data Sets .. - Pavlov, Smyth (2002) (Correct)
....below we use non negative coecients constrained to sum to 1. Experimental results on using di erent types of coecients (not shown here) indicate that using unconstrained coecients produces almost identical estimates in practice to the constrained case (agreeing with the results reported in [4] for regression) This general approach of learning model weights on a validation data set is known as stacking in the machine learning and statistics literature [28] and has been demonstrated to provide substantially improved predictive power over individual models for both regression [4] and ....
.... in [4] for regression) This general approach of learning model weights on a validation data set is known as stacking in the machine learning and statistics literature [28] and has been demonstrated to provide substantially improved predictive power over individual models for both regression [4] and density estimation [27] Our adaptation of stacking to the query approximation problem is quite straightforward. The main di erence with prior work on model combining (e.g. 27] is in optimization with respect to the query distribution (Q) In previous work the model coecients were ....
[Article contains additional citation context not shown here]
L. Breiman. Stacked regressions. Machine Learning, 24:49|64, 1996.
Relational Data Mining with Inductive Logic.. - Mooney, Melville, .. (2002) (3 citations) (Correct)
....method that is known to frequently create a more accurate ensemble than individual components, bagging [Breiman1996a] Bagging works by training each classifier on a random sample from the training set. Bagging has the important advantage that it is effective on unstable learning algorithms [Breiman1996b] where small variations in parameters can cause huge variations in the learned theories. This is the case with ILP. A second advantage is that it can be implemented in parallel trivially. Further details about our bagging approach within ILP, as well as our experimental methodology, can be found ....
Breiman, L. 1996b. Stacked Regressions. Machine Learning 24(1):49--64.
Relational Data Mining with Inductive Logic . . . - Mooney (2002) (Correct)
....on a popular method that is known to frequently create a more accurate ensemble than individual components, bagging [1] Bagging works by training each classifier on a random sample from the training set. Bagging has the important advantage that it is effective on unstable learning algorithms [2], where small variations in parameters can cause huge variations in the learned theories. This is the case with ILP. A second advantage is that it can be implemented in parallel trivially. Further details about our bagging approach within ILP, as well as our experimental methodology, can be found ....
L. Breiman. Stacked Regressions. Machine Learning, 24(1):49--64, 1996.
An Empirical Evaluation of Bagging in Inductive Logic.. - Dutra, Page, Costa.. (2002) (2 citations) (Correct)
....components, bagging [6] Bagging works by training each classifier on a random sample from the training set. In contrast to other well known techniques for ensemble generation, such as boosting [12] bagging has the important advantage that it is effective on unstable learning algorithms [7], where small variations in parameters can cause huge variations in the learned theories. This is the case with ILP. A second advantage is that it can be implemented in parallel trivially. We contrast bagging with a method we name different seeds, where we try to take advantage of the ....
....proposed for decision trees, where gains have been seen up to 25 classifiers. In our experiments we decided to extend our analysis up to 100 classifiers. The last problem concerns the combination algorithm. An effective combining scheme is often to simply average the predictions of the network [1, 7, 17, 18]. An alternate approach relies on a pre defined voting threshold. If the number of theories that cover an example is above or equal to the threshold, we say that the example is positive, otherwise the example is negative. Thresholds may range from i to the ensemble size. A voting threshold of 1 ....
L. Breiman. Stacked Regressions. Machine Learning, 24(1):49-64, 1996.
Distributed Data Mining Systems - Prodromidis (1999) (Correct)
....classifiers into an ensemble meta classifier by learning how they predict, i.e. by observing their input output behavior. 16 Several methods for integrating ensembles of models have been studied, including techniques that combine the set of models in some linear fashion [ Ali Pazzani, 1996; Breiman, 1994; 1996; Freund Schapire, 1995; Krogh Vedelsby, 1995; LeBlanc Tibshirani, 1993; Littlestone Warmuth, 1989; Opitz Shavlik, 1996; Perrone Cooper, 1993; Schapire, 1990; Tresp Taniguchi, 1995 ] techniques that employ referee functions to arbitrate among the predictions generated by the ....
Breiman, L. 1996. Stacked regressions. Machine Learning 24:41--48.
From Local Polynomial Approximation to Pointwise.. - Foi, Katkovnik (2006) (Correct)
No context found.
Breiman, L., Stacked regression, Machine Learning,vol. 24, pp. 49-64, 1996.
Spatially Adaptive Non-Gaussian Imaging via Fitted Local.. - Katkovnik, Spokoiny (2006) (Correct)
No context found.
L. Breiman, "Stacked regression," Machine Learning, 24 pp. 49-64, 1996.
Heterogenous Committees with Competence Analysis - Norbert Jankowski Krzysztof (Correct)
No context found.
L. Breiman. Stacked regressions. Machine Learning, 24:49-- 64, 1996.
Automatic Bias Learning: An Inquiry into the Inductive Basis of.. - Bensusan (1999) (1 citation) (Correct)
No context found.
Breiman, L. #1996#. Stacked regressions. Machine Learning, 24, 49#64.
Approximate Query Answering by Model Averaging - Dmitry Pavlov Nec (Correct)
No context found.
L. Breiman. Stacked regressions. Machine Learning, 24:49---64, 1996.
Dynamic Integration of Regression Models - Rooney, Patterson, Anand, Tsymbal (2004) (Correct)
No context found.
Breiman, L. 1996. Stacked Regression. Machine Learning, 24:49-64.
Combining Machine Learning and Hierarchical Structures for Text.. - Ruiz (2001) (1 citation) (Correct)
No context found.
L. Breiman. Stacked regressions. Machine Learning, 24(1):49--64, 1996. 146
Classifier Fusion for Outdoor Obstacle Detection - Dima, Vandapel, Hebert (Correct)
No context found.
L. Breiman, "Stacked regressions," Machine Learning, vol. 24, no. 1, 1996.
Unsupervised Feature Selection for Ensemble of Classifiers - Marisa Morita Luiz (Correct)
No context found.
L. Breiman. Stacked regressions. Machine Learning, 24(1):49--64, 1996.
Relational Data Mining with Inductive Logic Programming .. - Mooney, Melville.. (2002) (3 citations) (Correct)
No context found.
L. Breiman. Stacked Regressions. Machine Learning, 24(1):49--64, 1996.
Feature Selection and Classifier Ensembles: A Study on.. - Yu (2003) (Correct)
No context found.
L. Breiman. Stacked regressions. Machine Learning, 24:49--64, 1996. 21
Relational Data Mining with Inductive Logic . . . - Mooney (2004) (Correct)
No context found.
Breiman, L. 1996b. Stacked Regressions. Machine Learning 24(1):49--64.
Relational Data Mining with Inductive Logic Programming .. - Mooney, Melville.. (2002) (3 citations) (Correct)
No context found.
L. Breiman. Stacked Regressions. Machine Learning, 24(1):49--64, 1996.
Arbitrating Among Competing Classifiers Using Learned Referees - Ortega, Koppel, Argamon (1998) (7 citations) (Correct)
No context found.
Leo Breiman. Stacked regressions. Machine Learning, 24:49--64, 1996.
Classifier Fusion for Outdoor Obstacle Detection - Cristian Dima Nicolas (2004) (Correct)
No context found.
L. Breiman, "Stacked regressions," Machine Learning, vol. 24, no. 1, 1996.
Importance Sampled Learning Ensembles - Friedman, Popescu (2003) (Correct)
No context found.
Breiman,L. (1996b). Stacked Regressions. Machine Learning, 24 51-64.
Sensor and Classifier Fusion for Outdoor Obstacle.. - Dima, Vandapel, Hebert (2003) (Correct)
No context found.
L. Breiman. Stacked regressions. Machine Learning, 24(1):49--64, July 1996.
Multiagent Cooperative Learning of User - Preferences Adorjan Kiss (2001) (Correct)
No context found.
L. Breiman. Stacked regression. Machine Learning, 24:49, 1996.
Combining Discriminant Models with new Multi-Class SVMs - Guermeur (2000) (3 citations) (Correct)
No context found.
L. Breiman. Stacked Regressions. Machine Learning, 24:4964, 1996.
An Empirical Evaluation of Bagging in Inductive Logic.. - Dutra, Page, Costa.. (2002) (2 citations) (Correct)
No context found.
L. Breiman. Stacked Regressions. Machine Learning, 24(1):49-64, 1996.
First 50 documents Next 50
Online articles have much greater impact More about CiteSeer.IST Add search form to your site Submit documents Feedback
CiteSeer.IST - Copyright Penn State and NEC