حداقل‌سازی خطای پیش‌بینی مدل‌های طبقه‌بندی تصویر با استفاده از کدگذاری تنک و تطبیق دامنه

[1] J. Tahmoresnezhad and S. Hashemi, “A generalized kernel-based random k-sample sets method for transfer learning”, Iran J Sci Technol Trans Electrical Eng, vol. 39, pp. 193-207, 2015.

[2] J. Tahmoresnezhad  and S. Hashemi S, “An Efficient yet Effective Random Partitioning and Feature Weighting Approach for Transfer Learning”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 30, no. 2, 1651003, 2016.

[3] طاهره زارع بیدکی و محمدتقی صادقی، «بهینه‌سازی وزن‌ها در کرنل مرکب برای طبقه‌بند مبتنی بر نمایش تنک کرنلی»، مجله مهندسی برق دانشگاه تبریز، جلد 47، شماره 3، صفحات 1059-1072، 1396.

[4] Pereira, L. A., & da Silva Torres, R. “Semi-supervised transfer subspace for domain adaptation ”, Pattern Recognition, 75, pp.  235-249, 2018.

[5] Ishii, M., & Sato, A, “Joint optimization of feature transform and instance weighting for domain adaptation”,  In Neural Networks (IJCNN), International Joint Conference on IEEE,  pp. 3793-3799, 2017.

 [6] B. Gong, Y. Shi, F. Sha and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2066-2073, 2012.

[7] Cao, Y., Long, M., & Wang, J. “Unsupervised Domain Adaptation with Distribution Matching Machines”, In AAAI Conference on Artificial Intelligence, 2018.

[8] Herath, S., Harandi, M. T., & Porikli, F. “ Learning an Invariant Hilbert Space for Domain Adaptation”, In CVPR, pp. 3956-3965, 2017.

 [9] M. Ghifary, D. Balduzzi, W. B. Kleijn, and M. Zhang, “Scatter component analysis: A unified framework for domain adaptation and domain generalizayion”, IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1-1, 2016.

[10] B. Sun and K. Saenko. “Subspace distribution alignment for unsupervised domain adaptation”, in Proc.British Machine Vision Conference, 2015.

[11] S. Satpal and S. Sarawagi, “Domain adaptation of conditional

probability models via feature subsetting”, Proceedings of PKDD, vol.

4702, pp. 224-235, 2007.

[12] J. Tahmoresnezhad and S. Hashemi, “Visual domain adaptation via transfer feature learning”, KnowlInf Syst, vol. 50, no. 2, pp. 585-605, 2016.

[13] Ding, Z., & Fu, Y, “ Robust transfer metric learning for image classification”, IEEE Transactions on Image Processing, vol.  26, no. 2, 660-670, 2017.

[14] Jolliffe I, “Principal component analysis”, Wiley, vol. 2, pp. 433-459, 2002.

[15] مهرداد حیدری ارجلو، سید قدرت اله سیف السادات و مرتضی رزاز، «یک روش هوشمند تشخیص جزیره در شبکه توزیع دارای تولیدات پراکنده مبتنی‌بر تبدیل موجک و نزدیک‌ترین k-همسایگی (kNN) »، مجله مهندسی برق دانشگاه تبریز، جلد 43، شماره 1، صفحات 15-26، 1392.

[16] S. Si, D. Tao and B. Geng, “Bregman divergence-based regularization for transfer subspace learning”, IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 7, pp. 929-942, 2010.

[17] S. J. Pan, I. W. Tsang, J. T. Kwok and Q. Yang, “Domain adaptation via transfer component analysis”, IEEE Trans. Neural Netw, vol. 22, no. 2, pp. 199–210, 2011.

[19] B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars, “Unsupervised visual domain adaptation using subspace alignment”, in Proc. IEEE International Conference on Computer vision, pp. 2960-2967, 2013.

[20] Y. Xu, X. Fang, J. Wu, X. Li and D.  Zhang, “Discriminative transfer subspace learning via low-rank and sparse representation”, IEEE Transactions on Image Processing, vol. 25, no. 2,pp. 850-863, 2016.