L. Hansen and P. Salamon, "Neural Network Ensembles," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-- 1001, 1990. Home/Search Document Not in Database Summary Related Articles Check |
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A New Learning Scheme for Neural Network Ensembles - Haraldsson, Ohlsson (2002) (Correct)
....on Neural Networks I. INTRODUCTION Ensemble learning for neural networks is a subject of active research. It enables an increase in generalization performance by combining several individual networks trained on the same task. The ensemble approach has been justified both theoretically [1], 2] and empirically [3] The creation of an ensemble is often divided into two steps [4] the first being the judicious creation of the individual ensemble members and the second their appropriate combination to produce the ensemble output. Combining the outputs is clearly only relevant when ....
L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 993--1001, 1990.
Unknown - Pe Cep Ua (2002) (Correct)
....bootstrap replicates of the learning set and using these as new learning sets. This method, among some others [6, 7, 13, 16, 17] has shown the interest to combine multiple versions of a predictor to improve the precision of the results. These methods are motivated by the bias variance dilemma [2, 4, 5, 11, 14, 19]. Here the novel cost function do not minimize this kind of variance. We showed in [9] that, with the cost function described in this paper, the performance are improved for a single neural network. In the comparative study below the objective is to see if this improvement is preserved in a ....
L. K. Hansen and P. Salamon. Neural networks ensembles. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume 12, pages 993-1001, 1990.
Selection of Image Classifiers - Giorgio Giacinto Fabio (2000) (Correct)
....for each pixel, different classifiers make different classification errors. In particular, Hansen and Salamon proved that the image classification accuracy reached by combining different classifiers can outperform the best individual classifier, only if the classifiers make independent errors [2]. Unfortunately, in real image classification applications, it is usually difficult to design a set of classifiers satisfying such an assumption. On the other hand, one can verify experimentally that it is easier to design a classifier ensemble where, for each pixel, at least one classifier can ....
Hansen, L.K., and Salamon, P.: "Neural network ensembles". IEEE Trans. on Pattern Analysis and Machine Intelligence, 1990, 12(10), pp. 993-1001.
Design of Effective Multiple Classifier Systems by.. - Giacinto, Roli, Fumera (Correct)
....[1,2] However, the above also showed that MCSs are effective only if the classifiers forming them make different errors. As an example, Hansen and Salamon showed that classifiers combined by the majority rule can provide increases in classification accuracy only if they make independent errors [3]. Accordingly, most combination methods described in the literature assume that MCSs are made up of classifiers making independent classification errors. Unfortunately, the reported experimental results have pointed out that the creation of error independent classifiers is not a trivial task, in ....
Hansen, L. K., and P. Salamon, "Neural network ensembles", IEEE Trans. on PAMI, 12, 1990, pp. 993-1001.
A Theoretical Framework for Dynamic Classifier Selection - Giorgio Giacinto And (2000) (3 citations) (Correct)
....are then combined. The combination methods proposed in the literature are based on voting rules, statistical techniques, belief functions, and other classifier fusion schemes [1] Such methods usually assume that, for each pattern, different classifiers make different classification errors [2]. Unfortunately, in real pattern recognition applications, it is usually difficult to design a set of classifiers satisfying such an assumption. On the other hand, one can verify experimentally that it is easier to design a classifier ensemble where, for each pattern, at least one classifier can ....
Hansen, L.K., and Salamon, P.: "Neural network ensembles". IEEE Trans. on Pattern Analysis and Machine Intelligence, 1990, 12(10), pp. 993-1001.
Medical Diagnosis with C4.5 Rule Preceded by Artificial Neural.. - Zhou, Jiang (2003) (Correct)
....cancer are presented. In Section 4, some issues of C4.5 Rule PANE that should be further investigated are discussed. Finally in Section 5, the main contribution of this paper is summarized. II. METHODOLOGY A. Artificial Neural Network Ensemble In the beginning of the 1990s, Hansen and Salamon [12] showed that the generalization ability of learning systems based on artificial neural networks can be significantly improved through ensembling artificial neural networks, i.e. training multiple artificial neural networks and combining their predictions. Subsequently there appears a hot wave in ....
....a series of component networks whose mining data sets are determined by the performance of the former networks. Training instances that are wrongly predicted by the former networks will play more important roles in the mining of the later networks. As for combining component predictions, voting [ 12] is prevailing for classification while averaging [ 18] 19] is prevailing for regression. Voting regards the class label receiving the most number of votes as the final output of the ensemble. Averaging regards the average output of the component networks as the final output of the ensemble. ....
L.K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp.993 - 1001, 1990.
Time Series Prediction with Ensemble Models - Wichard, Ogorzalek (2004) (2 citations) (Correct)
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L. Hansen and P. Salamon, "Neural Network Ensembles," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993-- 1001, 1990.
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L. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 12, no. 10, pp. 993--1001, 1990.
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Hansen, L., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Patern Analysis and Machine Intelligence, 12, 993--1001.
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Hansen LK, Salamon P (1990) Neural network ensembles. IEEE Trans Pattern Anal 12(10):993-1001
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L.K. Hansen and P. Salamon, "Neural network ensembles", IEEE Trans. PAMI, vol.12, 9931001, 1990.
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L. K. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell., 12(10):993--1001, 1990.
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L. Hansen and P. Salamon, "Neural network ensembles," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 12, pp. 993--1001, 1990.
Schemes for Integrating Multiple Neural Networks for - Multispectral Data.. (Correct)
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L.K. Hansen and P. Salamon, "Neural network ensembles", IEEE Trans. PAMI, vol.12, 9931001, 1990.
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L.K. Hansen and P. Salamon, "Neural network ensembles, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no.10, pp.993--1001, 1990.
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Hansen, L. K., & Salamon, P. (1990). Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(19), 993-1001.
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L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 993--1001, Oct. 1990.
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L. K. Hansen and P. Salamon. Neural network ensembles. IEEE Trans. Neural Networks, 12(10):993--1001, 1990.
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L.K. Hansen and P. Salamon, "Neural network ensembles, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, no.10, pp.993--1001, 1990.
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L. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 993--1001, 1990.
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L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 993--1001, Dec. 1990.
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Hansen, L. K., and Salamon, P. (1990) Neural network ensembles, IEEE Pattern Anal. Machine Intell. 12, 993-- 1001.
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L. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Machine Intell., vol. 12, no. 10, pp. 993--1001, 1990.
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L.K. Hansen and P. Salamon, "Neural network ensembles", IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, pp. 993-1001, 1990.
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L.K. Hansen, P. Salamon, "Neural Network Ensemble", IEEE Trans. Pattern Analysis and Machine Leaming, vol. 12, 993- 1001, 1990.
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