參考文獻 |
[1] Y. Liu, H. Liu, B. Zhang and G. Wu, “Extraction of if-then rules from trained neural network and its application to earthquake prediction,” Proceedings of the Third IEEE International Conference on Cognitive Informatics, 2004.
[2] T. Kondo, J. Ueno, and S. Takao, “Medical image diagnosis of lung cancer by revised GMDH-type neural network self-selecting optimum neuron architectures,” System Integration (SII), IEEE/SICE International Symposium, 2011.
[3] N.L.D. Khoa, K. Sakakibara, and I. Nishikawa, “Stock Price Forecasting using Back Propagation Neural Networks with Time and Profit Based Adjusted Weight Factors,” SICE-ICASE, International Joint Conference, 2006.
[4] G.E. Hinton, and R.R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, Vol. 313. no. 5786, pp. 504 - 507, 28 July 2006.
[5] http://www.ling.fju.edu.tw/hearing/brain-into.htm
[6] 蘇木春、張孝德 機器學習 : 類神經網路.模糊系統以及基因演算法則,修訂第二版,全華圖書出版社,2004,ISBN 9572147374
[7] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning representations by back-propagating errors,” Nature 323 (6088): 533–536, 8 October 1986.
[8] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998
[9] I. Mrazova, and M. Kukacka, “Hybrid convolutional neural networks,” Industrial Informatics INDIN 2008. 6th IEEE International Conference, 2008.
[10] D. Ciresan, U. Meier, and J. Schmidhuber, “Multi-column deep neural networks for image classification,” in Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 3642-3649, 2012.
[11] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks”, Advances in neural information processing systems, pp. 1097-1105, 2012.
[12] M. Lin, Q. Chen, and S. Yan, “Network in network,” Computing Research Repository, 2013.
[13] K. Simonyan, and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” International Conference on Learning Representations, 2015.
[14] C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1-9, 2015.
[15] T. G. Dietterich, “Ensemble methods in machine learning,” in Multiple classifier systems, Springer Berlin Heidelberg, pp.1-15, 2000.
[16] Y. Freund, R. Schapire, and N. Abe, “A short introduction to boosting,” Journal-Japanese Society For Artificial Intelligence, 1999.
[17] Y. Liu, D. Zhang, G. Lu, and W. Y. Ma, “A survey of content-based image retrieval with high-level semantics,” Pattern Recognition vol. 40, no.1, pp. 262-282, 2007.
[18] X.S. Zhou, and T.S. Huang, “CBIR: from low-level features to high-level semantics,” Proceedings of the SPIE, Image and Video Communication and Processing, San Jose, CA, vol. 3974, pp. 426–431, 2000.
[19] Y. Chen, J.Z. Wang, and R. Krovetz, “An unsupervised learning approach to content-based image retrieval,” IEEE Proceedings of the International Symposium on Signal Processing and its Applications, pp. 197–200, 2003.
[20] A.W. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-based image retrieval at the end of the early years,” Pattern Analysis and Machine Intelligence, IEEE Transactions on 22.12, pp. 1349-1380, 2000.
[21] M.J. Swain, and D.H. Ballard, “Color Indexing,” International Journal of Computer Vision, pp. 11-32, 1991.
[22] M.A. Stricker, and M. Orengo, “Similarity of color images,” IS&T/SPIE′s Symposium on Electronic Imaging: Science & Technology. International Society for Optics and Photonics, 1995.
[23] G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vectors,” Proceedings of the fourth ACM international conference on Multimedia. ACM, 1997.
[24] M. Ferman, A.M. Tekalp, and R. Mehrotra, “Robust Color Histogram Descriptors for Video Segment Retrieval and Identification”, IEEE Transactions on Image Processing, vol. 11, no. 5, pp. 497-508, 2002.
[25] H. Tamura, S. Mori, T. Yamawaki, “Texture features corresponding to visual perception,” IEEE Transactions on Systems, Man and Cybernetics, vol. 8, no. 6, pp.460-473, 1978.
[26] T. Ojala, M. Pietikäinen, and D. Harwood, “Performance evaluation of texture measures with classification based on Kullback discrimination of distributions,” Proceedings of the 12th IAPR International Conference on Pattern Recognition, vol. 1, pp. 582 – 585, 1994.
[27] X. Wang, T.X. Han, and S. Yan, “An HOG-LBP human detector with partial occlusion handling,” 2009 IEEE 12th International Conference on Computer Vision, 2009.
[28] C.H. Kuo, Y.H. Chou, and P.C. Chang, “Using Deep Convolutional Neural Networks for Image Retrieval,” Visual Information Processing and Communication VI, February 2016
[29] R. Xia, Y. Pan, H. Lai, C. Liu, and S. Yan, “Supervised Hashing for Image Retrieval via Image Representation Learning,” in Proceedings of the AAAI Conference on Artificial Intelligence, 2014.
[30] D. Cireşan, U. Meier, and J. Masci, “Multi-column deep neural network for traffic sign classification,” Neural Networks, 2012.
[31] A. Coates, A.Y. Ng, and H. Lee, “An analysis of single-layer networks in unsupervised feature learning,” International conference on artificial intelligence and statistics, 2011.
[32] I.J. Goodfellow, D. Warde-Farley, M. Mirza, A. Courville, and Y. Bengio, “Maxout networks,” arXiv preprint arXiv:1302.4389, 2013.
[33] Y. Jia, et al., “Caffe: Convolutional architecture for fast feature embedding,” In Proceedings of the ACM International Conference on Multimedia, 2014.
[34] A. Torralba, R. Fergus, and W. Freeman, “80 million tiny images: A large data set for nonparametric object and scene recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 11, pp. 1958-1970, 2008. |