Ensembles of Classifiers from Spatially Disjoint Data-Reference-Cited by-同舟云学术

Ensembles of Classifiers from Spatially Disjoint Data

Published:2005 Issue: Volume: Page:196-205
ISSN:0302-9743
Container-title:Multiple Classifier Systems
language:
Short-container-title:

Author:

Banfield Robert E.,Hall Lawrence O.,Bowyer Kevin W.,Kegelmeyer W. Philip

Publisher

Springer Berlin Heidelberg

Link

http://link.springer.com/content/pdf/10.1007/11494683_20

Reference11 articles.

1. Hall, L.O., Bhadoria, D., Bowyer, K.W.: Learning a model from spatially disjoint data. In: 2004 IEEE International Conference on Systems, Man and Cybernetics (October 2004)

2. National Nuclear Security Administration in collaboration with Sandia, Lawrence Livermore, and Los Alamos National Laboratories, http://www.sandia.gov/NNSA/ASC/

3. Lee, B.S., Snapp, R.R., Musick, R.: Toward a query language on simulation mesh data: an object oriented approach. In: Proceedings of the International Conference on Database Systems for Advanced Applications, Hong Kong (April 2001)

4. Breiman, L.: Random forests. Machine Learning 45, 5–32 (2001)

5. Banfield, R.E., Hall, L.O., Bowyer, K.W., Bhadoria, D., Kegelmeyer, W.P., Eschrich, S.: A comparison of ensemble creation techniques. In: The Fifth International Conference on Multiple Classifier Systems, Cagliari, Italy (June 2004)

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Prediction of liquid ammonia yield using a novel deep learning‐based heterogeneous pruning ensemble model;Asia-Pacific Journal of Chemical Engineering;2020-03

2. Detecting and ordering salient regions;Data Mining and Knowledge Discovery;2010-08-03

3. A divide-and-conquer recursive approach for scaling up instance selection algorithms;Data Mining and Knowledge Discovery;2008-12-12

4. Automatically countering imbalance and its empirical relationship to cost;Data Mining and Knowledge Discovery;2008-02-17