Comparative Investigation of Learning Algorithms for Image Classification with Small Dataset

References

• Abadi, M.; Agarwal, A.; Barham, P.; Brevdo, E.; Chen, Z.; Citro, C.; Corrado, G.S.; Davis, A.; Dean, J.;Devin, M.; et al. 2016. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. http://arxiv.org/abs/1603.04467.
   Google Scholar
• Andrej Karpathy, George Toderici, Sanketh Shetty, Thomas Leung, Rahul Sukthankar, Li Fei-Fei. Proceedings of International Computer Vision and Pattern Recognition (CVPR 2014), In 2014, it took place at the Greater Columbus Convention Center in Columbus, Ohio. Main Conference: June 24–27, 2014
   Google Scholar
• Bengio, Y. 2012. Practical recommendations for gradient-based training of deep architectures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, Berlin, Heidelberg. 7700 LECTU:437–78.
   Google Scholar
• Collobert, R., and J. Weston. 2008. A unified architecture for natural language processing. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 2008. http://portal.acm.org/citation.cfm?doid=1390156.1390177.
   Google Scholar
• Dozat, T. 2016. Incorporating nesterov momentum into adam. ICLR Workshopno 1:2013–16.
   Google Scholar
• Duchi, J., E. Hazan, and Y. Singer. 2011. Adaptive subgradient methods for online learning and stochastic optimization. Journal of Machine Learning Research 12:2121–59. http://jmlr.org/papers/v12/duchi11a.html.
   Web of Science ®Google Scholar
• Glorot, X., and Y. Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research 9:249–56.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016 December. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 770–78.
   Google Scholar
• Hinton, G., L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups. IEEE Signal Processing Magazine 29 (6):82–97. http://ieeexplore.ieee.org/abstract/document/6296526/.
   Web of Science ®Google Scholar
• Hinton, G. E., N. Srivastava, and K. Swersky. 2012. Lecture 6a- overview of mini-batch gradient descent. COURSERA: Neural Networks for Machine Learning 31. http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf.
   Google Scholar
• Ioffe, S., and C. Szegedy. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. 32nd International Conference on Machine Learning, ICML, 1:448–56.Lille, France. http://arxiv.org/abs/1502.03167.
   Google Scholar
• Iqbal, I., G. Mustafa, and J. Ma. 2020. Deep learning-based morphological classification of human sperm heads. Diagnostics 10 (5):325. https://www.mdpi.com/2075-4418/10/5/325.
   Web of Science ®Google Scholar
• Iqbal, I., G. Shahzad, N. Rafiq, G. Mustafa, and J. Ma. 2020. Deep learning-based automated detection of human knee joint’s synovial fluid from magnetic resonance images with transfer Learning. IET Image Processing 14 (10):1990–98. doi:10.1049/iet-ipr.2019.1646.
   Web of Science ®Google Scholar
• Iqbal, I., M. Younus, K. Walayat, M. U. Kakar, and J. Ma. 2021. Automated multi-class classification of skin lesions through deep convolutional neural network with dermoscopic images. Computerized Medical Imaging and Graphics 88:101843. doi:10.1016/j.compmedimag.2020.101843.
   PubMed Web of Science ®Google Scholar
• Kingma, D. P., and J. L. Ba. 2015. Adam: A method for stochastic optimization. 3rd International Conference on Learning Representations. San Diego, USA. http://arxiv.org/abs/1412.6980.
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems 2:1097–105.
   Google Scholar
• LeCun, Y., L. Bottou, Y. Bengio, and P. Haffner. 1998. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86:2278–323. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf.
   Web of Science ®Google Scholar
• Lecun, Y., Y. Bengio, and G. Hinton. 2015. Deep Learning. Nature 521 (7553):436–44. doi:10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• Mcculloch, W. S., and W. Pitts. 1943. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 5 (4):99–115. doi:10.1007/BF02478259.
   Google Scholar
• Nair, V., and G. E. Hinton. 2010. Rectified linear units improve restricted boltzmann machines. Proceedings of the 27th International Conference on Machine Learning, Haifa, Palestine. 807–14.
   Google Scholar
• Nene, S. A., S. K. Nayar, and H. Murase. 1996. Columbia University Image Library. http://www1.cs.columbia.edu/CAVE/software/softlib/coil-100.php
   Google Scholar
• Nesterov, Y. 1983. A method for unconstrained convex minimization problem with the rate of convergence O (1/K2). Doklady Akademii Nauk SSSR 269 (3):543–47.
   Google Scholar
• Qian, N. 1999. On the momentum term in gradient descent learning algorithms. Neural Networks 12 (1):145–51. doi:10.1016/S0893-6080(98)00116-6.
   PubMed Web of Science ®Google Scholar
• Robbins, H., and S. Monro. 1951. A stochstic approximation method. The Annals of Mathematical Statistics 22 (3):400–07. doi:10.1214/aoms/1177729586.
   Google Scholar
• Rumelhard, D. E., G. E. Hinton, and R. J. Williams. 1986. Learning representations by back-propagating errors. Letters To Nature 323:533–36. doi:10.1038/323533a0.
   Web of Science ®Google Scholar
• Samuel, A. L. 1959. Some Studies in machine learning using the game of checkers. IBM Journal of Research and Development 3 (3):210–29. doi:10.1147/rd.33.0210.
   Web of Science ®Google Scholar
• Sashank Reddi, Satyen Kale, Sanjiv Kumar. Sixth International Conference on Learning Representations. Vancouver Convention Center, Vancouver CANADA. Mon Apr 30th through May 3rd, 2018
   Google Scholar
• Simonyan, K., and A. Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015 - Conference Track Proceedings. ICLR 2015 held May 7 - 9, 2015 in San Diego, CA.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. 2015. Going Deeper with Convolutions. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 07-12-June:1–9. Boston, USA.
   Google Scholar
• Townsend, J. T. 1971. Theoretical analysis of an alphabet confusion matrix. Attention Perception & Psychophysics 9 (1A):40–50. doi:10.3758/BF03213026.
   Google Scholar
• Zeiler, M. D. 2012. ADADELTA: An adaptive learning rate method. http://arxiv.org/abs/1212.5701.
   Google Scholar