publication . Preprint . Part of book or chapter of book . 2018

Deep Super Learner: A Deep Ensemble for Classification Problems

Steven Young; Tamer Abdou; Ayse Bener;
Open Access English
  • Published: 06 Mar 2018
Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datasets. While traditional machine learning algorithms can address these drawbacks, they are not typically capable of the performance levels achieved by deep neural networks. To ...
arXiv: Computer Science::Machine Learning
free text keywords: Computer Science - Learning, Statistics - Machine Learning, Deep learning, Big data, business.industry, business, Ensemble learning, Transparency (graphic), Deep neural networks, Artificial neural network, Machine learning, computer.software_genre, computer, Artificial intelligence, Computer science, Convergence (routing)
Related Organizations
Download fromView all 2 versions
Part of book or chapter of book
Provider: UnpayWall
Part of book or chapter of book
Provider: Crossref
16 references, page 1 of 2

1. Bengio, Y., Courville, A., Vincent, P.: Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence 35(8) (8 2013) 1798{1828

2. Langkvist, M., Karlsson, L., Lout , A.: A Review of Unsupervised Feature Learning and Deep Learning for Time-series Modeling. Pattern Recognition Letters 42 (2014) 11{24

3. Schmidhuber, J.: Multi-column Deep Neural Networks for Image Classi cation. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12), Washington, DC, USA, IEEE Computer Society (2012) 3642{3649 [OpenAIRE]

4. Bengio, Y.: Learning Deep Architectures for AI. Foundations and Trends R in Machine Learning 2(1) (1 2009) 1{127

5. Zhou, Z.H., Feng, J.: Deep forest: Towards an Alternative to Deep Neural Networks. In: Proceedings of the 26th International Joint Conference on Arti cial Intelligence (IJCAI '17), Melbourne, Australia (2017) 3553{3559

6. Sussillo, D., Barak, O.: Opening the Black Box: Low-Dimensional Dynamics in High-Dimensional Recurrent Neural Networks. Neural Computation 25(3) (2013) 626{649

7. Farrelly, C.M.: Deep vs. Diverse Architectures for Classi cation Problems. (2017) [OpenAIRE]

8. Seni, G., Elder, J.F.: Ensemble Methods in Data Mining: Improving Accuracy Through Combining Predictions. In Grossman, R., ed.: Synthesis Lectures on Data Mining and Knowledge Discovery. Morgan & Claypool (2010) [OpenAIRE]

9. Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, California, USA, ACM (2016) 785{794

10. Xie, J., Rojkova, V., Pal, S., Coggeshall, S.: A Combination of Boosting and Bagging for KDD Cup 2009 - Fast Scoring on a Large Database. The Journal of Machine Learning Research (JMLR) 7 (2009) 35{43

11. van der Laan, M.J., Polley, E.C., Hubbard, A.E.: Super Learner. Statistical Applications in Genetics and Molecular Biology 6(1) (1 2007)

12. Zhou, Z.H.: Ensemble Methods: Foundations and Algorithms. illustrate edn. Chapman & Hall/CRC. Machine Learning & Pattern Recognition Series, Boca Raton, FL (2012)

13. Lessmann, S., Baesens, B., Mues, C., Pietsch, S.: Benchmarking Classi cation Models for Software Defect Prediction: A Proposed Framework and Novel Findings. IEEE Transactions on Software Engineering 34(4) (7 2008) 485{496 [OpenAIRE]

14. Maas, A.L., Daly, R.E., Pham, P.T., Huang, D., Ng, A.Y., Potts, C.: Learning Word Vectors for Sentiment Analysis. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. HLT '11, Portland, Oregon (2011) 142{150

15. Lecun, Y., Bottou, L., Bengio, Y., Ha ner, P.: Gradient-based Learning Applied to Document Recognition. Proceedings of the IEEE 86(11) (1998) 2278{2324

16 references, page 1 of 2
Powered by OpenAIRE Open Research Graph
Any information missing or wrong?Report an Issue
publication . Preprint . Part of book or chapter of book . 2018

Deep Super Learner: A Deep Ensemble for Classification Problems

Steven Young; Tamer Abdou; Ayse Bener;