Journal of Engineering and Applied Sciences

Year: 2017
Volume: 12
Issue: 12 SI
Page No. 9467 - 9472

A Survey on Inferences from Deep Learning Algorithms

Authors : Deepali Vora and Kamatchi Iyer

References

Al-Rfou, R., G. Alain, A. Almahairi, C. Angermueller and D. Bahdanau et al., 2016. Theano: A python framework for fast computation of mathematical expressions. MSc Thesis, Cornell University, Ithaca, New York, USA.

Alpaydin, E., 2004. Introduction to Machine Learning. MIT Press, Cambridge, Massachusetts, UK., ISBN:13-9780262012119, Pages: 415.

Bengio, Y., 2009. Learning deep architectures for AI. Found. Trends Mach. Learn., 2: 1-127.
CrossRef  |  Direct Link  |  

Bengio, Y., P. Lamblin, D. Popovici and H. Larochelle, 2007. Greedy Layer-Wise Training of Deep Networks. In: Advances in Neural Information Processing Systems, Scholkopf, B., J. Platt and T. Hofmann (Eds.). MIT Press, Cambridge, Massachusetts, ISBN-13:978-0-262-19568-3, pp: 153-160.

Cho, K., A. Courville and Y. Bengio, 2015. Describing multimedia content using attention-based encoder-decoder networks. IEEE. Trans. Multimedia, 17: 1875-1886.
CrossRef  |  Direct Link  |  

David, O.E. and N.S. Netanyahu, 2015. Deepsign: Deep learning for automatic malware signature generation and classification. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), July 12-17, 2015, IEEE, Killarney, Ireland, ISBN:978-1-4799-1960-4, pp: 1-8.

Deng, Y., Z. Ren, Y. Kong, F. Bao and Q. Dai, 2016. A hierarchical fused fuzzy deep neural network for data classification. IEEE. Trans. Fuzzy Syst., 25: 1006-1012.
CrossRef  |  Direct Link  |  

Guo, B., R. Zhang, G. Xu, C. Shi and L. Yang, 2015. Predicting students performance in educational data mining. Proceedings of the 2015 International Symposium on Educational Technology (ISET), July 27-29, 2015, IEEE, Wuhan, China, ISBN:978-1-4673-7370-8, pp: 125-128.

Guo, X., H. Huang and J. Zhang, 2014. Comparison of different variants of restricted Boltzmann machines. Proceedings of the 2014 2nd International Conference on Information Technology and Electronic Commerce (ICITEC), December 20-21, 2014, IEEE, Dalian, China, ISBN:978-1-4799-5299-1, pp: 239-242.

Jia, Y., E. Shelhamer, J. Donahue, S. Karayev and J. Long et al., 2014. Caffe: Convolutional architecture for fast feature embedding. Proceedings of the 22nd ACM International Conference on Multimedia, November 03-07, 2014, ACM, Orlando, Florida, USA., ISBN:978-1-4503-3063-3, pp: 675-678.

Ju, Y., J. Guo and S. Liu, 2015. A deep learning method combined sparse autoencoder with SVM. Proceedings of the 2015 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), September 17-19, 2015, IEEE, Xi'an, China, ISBN:978-1-4673-9200-6, pp: 257-260.

Kim, S., M. Lee and J. Shen, 2015. A novel deep learning by combining discriminative model with generative model. Proceedings of the 2015 International Joint Conference on Neural Networks (IJCNN), July 12-17, 2015, IEEE, Killarney, Ireland, ISBN:978-1-4799-1960-4, pp: 1-6.

Kuwata, K. and R. Shibasaki, 2015. Estimating crop yields with deep learning and remotely sensed data. Proceedings of the 2015 IEEE International Conference on Geoscience and Remote Sensing Symposium (IGARSS), July 26-31, 2015, IEEE, Milan, Italy, ISBN:978-1-4799-7929-5, pp: 858-861.

Larochelle, H. and Y. Bengio, 2008. Classification using discriminative restricted Boltzmann machines. Proceedings of the 25th International Conference on Machine Learning, July 05-09, 2008, ACM, Helsinki, Finland, ISBN:978-1-60558-205-4, pp: 536-543.

Lauzon, F.Q., 2012. An introduction to deep learning. Proceedings of the 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), July 2-5, 2012, IEEE, Montreal, Québec, Canada, ISBN:978-1-4673-0381-1, pp: 1438-1439.

Li, D., Y. Tian and H. Xu, 2014. Deep twin support vector machine. Proceedings of the 2014 IEEE International Conference on Data Mining Workshop (ICDMW), December 14, 2014, IEEE, Shenzhen, China, ISBN:978-1-4799-4274-9, pp: 65-73.

Li, K., J. Gao, S. Guo, N. Du and X. Li et al., 2014. LRBM: A restricted Boltzmann machine based approach for representation learning on linked data. Proceedings of the 2014 IEEE International Conference on Data Mining (ICDM), December 14-17, 2014, IEEE, Shenzhen, China, ISBN:978-1-4799-4302-9, pp: 300-309.

Lv, Y., Y. Duan, W. Kang, Z. Li and F.Y. Wang, 2015. Traffic flow prediction with big data: A deep learning approach. IEEE. Trans. Intell. Transp. Syst., 16: 865-873.
CrossRef  |  Direct Link  |  

Najafabadi, M.M., F. Villanustre, T.M. Khoshgoftaar, N. Seliya and R. Wald et al., 2015. Deep learning applications and challenges in big data analytics. J. Big Data, 2: 1-21.
CrossRef  |  Direct Link  |  

Nguyen, K., C. Fookes and S. Sridharan, 2015. Improving deep convolutional neural networks with unsupervised feature learning. Proceedings of the 2015 IEEE International Conference on Image Processing (ICIP), September 27-30, 2015, IEEE, Québec, Canada, ISBN:978-1-4799-8339-1, pp: 2270-2274.

Niimi, A., 2015. Deep learning for credit card data analysis. Proceedings of the 2015 World Congress on Internet Security (WorldCIS), October 19-21, 2015, IEEE, Dublin, Ireland, ISBN:978-1-908320-50-6, pp: 73-77.

Qiu, X., L. Zhang, Y. Ren, P.N. Suganthan and G. Amaratunga, 2014. Ensemble deep learning for regression and time series forecasting. Proceedings of the 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL), December 9-12, 2014, IEEE, Orlando, Florida, USA., ISBN:978-1-4799-4512-2, pp: 1-6.

Rajanna, A.R., K. Aryafar, A. Shokoufandeh and R. Ptucha, 2015. Deep neural networks: A case study for music genre classification. Proceedings of the 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), December 9-11, 2015, IEEE, Miami, Florida, USA., ISBN:978-1-5090-0287-0, pp: 655-660.

Salakhutdinov, R. and G. Hinton, 2009. Deep Boltzmann machines. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, April 16-18, 2009, JMLR, Florida, USA., pp: 448-455.

Vincent, P., H. Larochelle, I. Lajoie, Y. Bengio and P.A. Manzagol, 2010. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res., 11: 3371-3408.
Direct Link  |  

Vincent, P., H. Larochelle, Y. Bengio and P.A. Manzagol, 2008. Extracting and composing robust features with denoising autoencoders. Proceedings of the 25th International Conference on Machine Learning, July 05-09, 2008, ACM, Helsinki, Finland, ISBN:978-1-60558-205-4, pp: 1096-1103.

Wilamowski, B.M., B. Wu and J. Korniak, 2016. Big data and deep learning. Proceedings of the 2016 IEEE 20th Jubilee International Conference on Intelligent Engineering Systems (INES), June 30-July 2, 2016, IEEE, Budapest, Hungary, ISBN:978-1-5090-1216-9, pp: 11-16.

Zhang, L.M., 2015. Genetic deep neural networks using different activation functions for financial data mining. Proceedings of the 2015 IEEE International Conference on Big Data (Big Data), October 29-November 1, 2015, IEEE, Santa Clara, Cllifornia, USA., ISBN:978-1-4799-9926-2, pp: 2849-2851.

Zhang, Q., L.T. Yang and Z. Chen, 2016. Deep computation model for unsupervised feature learning on big data. IEEE. Trans. Serv. Comput., 9: 161-171.
CrossRef  |  Direct Link  |  

Zhou, S., Q. Chen and X. Wang, 2013. Active deep learning method for semi-supervised sentiment classification. Neurocomputing, 120: 536-546.
Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved