ANALYTICAL STUDY OF IMAGE CLASSSIFICATION USING DEEP LEARNING
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References
Wei Wang, Gang Chen, Haibo Chen.Deep Learning at Scale and at Ease. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM). 12 (4s), 69-94 (2016).
Yann LeCun, Yoshua Bengio, Geoffrey Hinton. Deep Learning. Nature. 521 (1), 436-444 (2015).
Geoffrey E. Hinton, Simon Osindero, Yee-Whye Teh. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation. 18 (1), 1527–1554 (2006).
Li Deng and Dong Yu. Deep Learning Methods and Applications. Foundations and Trends in Signal Processing. 7 (3-4), 197-387 (2013).
Tom M. Mitchell. The Discipline of Machine Learning. CMU-ML. 06 (1), 108 (2006).
Geoffrey Hinton Where do features come from?. Canada: Department of Computer Science, University of Toronto. 1-33 (2013).
Rich Caruana and Alexandru Niculescu-Mizil. An Empirical Comparison of Supervised Learning Algorithms. International Con- ference on Machine Learning, Pittsburgh, PA, 2006. 23 (1), 1-8 (2006).
Seema Sharma, Jitendra Agrawal, Shikha Agarwal, Sanjeev Sharma. Machine Learning Techniques for Data Mining: A Survey. IEEE. 978-1-4799-1597 (2), 2-13 (2013).
Alex Krizhevsky and Georey E. Hinton. Using Very Deep Autoencoders for Content-Based Image Retrieval. University of Toronto - Department of Computer Science. 2 (18), 34-56 (2011).
Wei-Lun Chao. Machine Learning Tutorial. Taiwan: DISP Lab, Graduate Institute of Communication Engineering, National Taiwan University. 1-56 (2011).
Wei Wang. Big Data, Big Challenges. IEEE. 11 (3), 34-56 (2014).
Keiron O’Shea and Ryan Nash .An Introduction to Convolutional Neural Networks.arXiv:1511.08458v2 (2015).
Samer hifazi, Rishi kumar , and Chris rowen. Using convolutional neural networks for image recognition (2015).
Abdelkrim, A., & Loussaief, S. Machine learning framework for image classification. 2016 7th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 58-61 (2016).
Sandeep chaplot, C.M Patnaik, N.R jagannanthan. Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network. 86-92 (2006).
Waseem Rawat and Zenghui Wang. Deep Convolutional Neural Networks for Image Classification; A Comprehensive Review. 29:9, 2352-2449 (2017).
Bo Zhao1,2 Jiashi Feng2 Xiao Wu1 Shuicheng Yan2. A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation. 119-135 (2017).
Ansam a.abdul hussien. Comparison of machine learning algorithms to classify web pages. Vol. 8, No. 11 (2017).
Marée R., Geurts P., Visimberga G., Piater J., Wehenkel L. A Comparison of Generic Machine Learning Algorithms for Image Classification. In: Coenen F., Preece A., Macintosh A. (eds) Research and Development in Intelligent Systems XX. Springer, London (2004).
Bhaskar, J., & Patel, A.P. Image Classification using Convolutional Neural Network (2016).
Yim J., Ju J., Jung H., Kim J. Image Classification Using Convolutional Neural Networks With Multi-stage Feature. In: Kim JH., Yang W., Jo J., Sincak P., Myung H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham (2015).
Weis, Martin & Rumpf, T & Gerhards, Roland & Plümer, Lutz. Comparison of different classification algorithms for weed detection from images based on shape parameters. ISSN 0947-7314 (2018).
Al-Saffar, A.A.M., Tao, H. and Talab, M.A.,October. Review of deep convolution neural network in image classification. In Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2017 International Conference on (pp. 26-31). IEEE (2017).
Bayot, R. and Gonçalves, T.A Survey on Object Classification using Convolutional Neural Networks(2015).
Lee, A.,.Comparing Deep Neural Networks and Traditional Vision Algorithms in Mobile Robotics. Swarthmore College(2015).