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Ensemble learning combines several individual models to obtain better generalization performance.
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An ensemble approach for incremental learning in nonstationary environments,
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Selective negative correlation learning approach to incremental learning,
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Multi-label classification: An overview,
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Super learner,
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Understanding the difficulty of training deep feedforward neural networks,
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Ensemble-based classifiers,
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A review of ensemble methods in bioinformatics,
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Incremental learning by heterogeneous bagging ensemble,
Q. L. Zhao, Y. H. Jiang, M. Xu, · 2010
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Predicting gene function using hierarchical multi-label decision tree ensembles,
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A survey of clustering ensemble algorithms,
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Deep convex net: A scalable architecture for speech pattern classification,
L. Deng, D. Yu, · 2011
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Multi-label ensemble learning,
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Classifier chains for multi-label classification,
J. Read, B. Pfahringer, G. Holmes, E. Frank, · 2011
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Imagenet classification with deep convolutional neural networks,
A. Krizhevsky, I. Sutskever, G. E. Hinton, · 2012
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Ensemble approaches for regression: A survey,
J. Mendes-Moreira, C. Soares, A. M. Jorge, J. F. D. Sousa, · 2012
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V. Pisetta, New Insights into Decision Trees Ensembles, Ph.D. thesis, Lyon 2, 2012
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Scalable stacking and learning for building deep architectures,
L. L. Deng, D. Yu, J. Platt, · 2012
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Use of kernel deep convex networks and end-to-end learning for spoken language understanding,
L. Deng, G. Tur, X. He, D. Hakkani-Tur, · 2012
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Inquire and diagnose: Neural symptom checking ensemble using deep reinforcement learning,
K.-F. Tang, H.-C. Kao, C.-N. Chou, E. Y. Chang, · 2016
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Bier-boosting independent embeddings robustly,
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A. Mosca, G. D. Magoulas, · 2017
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Adanet: Adaptive structural learning of artificial neural networks (2017) 874–883
C. Cortes, X. Gonzalvo, V. Kuznetsov, M. Mohri, S. Yang, · 2017
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Visual representation and classification by learning group sparse deep stacking network,
J. Li, H. Chang, J. Yang, W. Luo, Y. Fu, · 2017
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A deep architecture with bilinear modeling of hidden representations: Applications to phonetic recognition,
B. Hutchinson, L. Deng, D. Yu, · 2012
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Tensor deep stacking networks,
B. Hutchinson, L. L. Deng, D. Yu, · 2012
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Multi-column deep neural networks for image classification,
D. Ciregan, U. Meier, J. Schmidhuber, · 2012
Cited alongside, same era.
Multi-label ensemble based on variable pairwise constraint projection,
P. Li, H. Li, M. Wu, · 2012
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Transductive multi-label ensemble classification for protein function prediction,
G. Yu, C. Domeniconi, H. Rangwala, G. Zhang, Z. Yu, · 2012
Cited alongside, same era.
Random features for Kernel Deep Convex Network,
P.-S. Huang, L. Deng, M. Hasegawa-Johnson, X. He, · 2013
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G. Wang, G. Zhang, K. S. Choi, J. Lu, · 2017
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Z.-H. Zhou, J. Feng, · 2017
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Regularization of Neural Networks using DropConnect,
L. Wan, M. Zeiler, S. Zhang, Y. L. Cun, R. Fergus, · 2017
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Snapshot ensembles: Train 1, get M for free,
G. Huang, Y. Li, G. Pleiss, Z. Liu, J. E. Hopcroft, K. Q. Weinberger, · 2017
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Branchout: Regularization for online ensemble tracking with convolutional neural networks,
B. Han, J. Sim, H. Adam, · 2017
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Ensemble feature selection: homogeneous and heterogeneous approaches,
B. Seijo-Pardo, I. Porto-Díaz, V. Bolón-Canedo, A. Alonso-Betanzos, · 2017
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Semi-supervised ensemble DNN acoustic model training,
S. Li, X. Lu, S. Sakai, M. Mimura, T. Kawahara, · 2017
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Ensemble application of convolutional and recurrent neural networks for multi-label text categorization,
G. Chen, D. Ye, Z. Xing, J. Chen, E. Cambria, · 2017
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Ensemble learning: A survey,
O. Sagi, L. Rokach, · 2018
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On building ensembles of stacked denoising auto-encoding classifiers and their further improvement,
R. F. Alvear-Sandoval, A. R. Figueiras-Vidal, · 2018
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Deep boosting for image denoising,
C. Chen, Z. Xiong, X. Tian, F. Wu, · 2018
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Sparse deep stacking network for fault diagnosis of motor,
C. Sun, M. Ma, Z. Zhao, X. Chen, · 2018
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Crowd counting with deep negative correlation learning,
Z. Shi, L. Zhang, Y. Liu, X. Cao, Y. Ye, M.-M. Cheng, G. Zheng, · 2018
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A deep learning algorithm for prediction of age-related eye disease study severity scale for age-related macular degeneration from color fundus photography,
F. Grassmann, J. Mengelkamp, C. Brandl, S. Harsch, M. E. Zimmermann, B. Linkohr, A. Peters, I. M. Heid, C. Palm, B. H. Weber, · 2018
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Heterogeneous ensemble for default prediction of peer-to-peer lending in china,
W. Li, S. Ding, Y. Chen, S. Yang, · 2018
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Deep learning-and word embedding-based heterogeneous classifier ensembles for text classification,
Z. H. Kilimci, S. Akyokus, · 2018
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Ensemble incremental learning random vector functional link network for short-term electric load forecasting,
X. Qiu, P. N. Suganthan, G. A. Amaratunga, · 2018
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The relative performance of ensemble methods with deep convolutional neural networks for image classification,
C. Ju, A. Bibaut, M. van der Laan, · 2018
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Ssel-ade: a semi-supervised ensemble learning framework for extracting adverse drug events from social media,
J. Liu, S. Zhao, G. Wang, · 2018
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Ensemble network architecture for deep reinforcement learning,
X.-l. Chen, L. Cao, C.-x. Li, Z.-x. Xu, J. Lai, · 2018
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Real-world image denoising with deep boosting,
C. Chen, Z. Xiong, X. Tian, Z.-J. Zha, F. Wu, · 2019
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Hibster: Hierarchical boosted deep metric learning for image retrieval,
G. Waltner, M. Opitz, H. Possegger, H. Bischof, · 2019
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Residual networks behave like boosting algorithms,
C. Siu, · 2019
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Deep stacked hierarchical multi-patch network for image deblurring,
H. Zhang, Y. Dai, H. Li, P. Koniusz, · 2019
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A novel deep stacking least squares support vector machine for rolling bearing fault diagnosis,
X. Li, Y. Yang, H. Pan, J. Cheng, J. Cheng, · 2019
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Stacking-based deep neural network: Deep analytic network for pattern classification,
C.-Y. Low, J. Park, A. B.-J. Teoh, · 2019
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Nonlinear Regression via Deep Negative Correlation Learning,
L. Zhang, Z. Shi, M.-M. Cheng, Y. Liu, J.-W. Bian, J. T. Zhou, G. Zheng, Z. Zeng, · 2019
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ENAET: self-trained ensemble autoencoding transformations for semi-supervised learning,
X. Wang, D. Kihara, J. Luo, G.-J. Qi, · 2019
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Semi-supervised deep coupled ensemble learning with classification landmark exploration,
J. Li, S. Wu, C. Liu, Z. Yu, H.-S. Wong, · 2019
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An evolutionary approach to build ensembles of multi-label classifiers,
J. M. Moyano, E. L. Gibaja, K. J. Cios, S. Ventura, · 2019
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Propensity score prediction for electronic healthcare databases using super learner and high-dimensional propensity score methods,
C. Ju, M. Combs, S. D. Lendle, J. M. Franklin, R. Wyss, S. Schneeweiss, M. J. van der Laan, · 2019
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Ensemble-based deep reinforcement learning for chatbots,
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Ensemble deep learning in bioinformatics,
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