Open Access   Article Go Back

Transfer Learning:Approaches and Methodologies

Swati Sucharita Barik1 , Mamata Garanayak2 , Sasmita Kumari Nayak3

Section:Survey Paper, Product Type: Journal Paper
Volume-7 , Issue-6 , Page no. 852-855, Jun-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i6.852855

Online published on Jun 30, 2019

Copyright © Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak, “Transfer Learning:Approaches and Methodologies,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.852-855, 2019.

MLA Style Citation: Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak "Transfer Learning:Approaches and Methodologies." International Journal of Computer Sciences and Engineering 7.6 (2019): 852-855.

APA Style Citation: Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak, (2019). Transfer Learning:Approaches and Methodologies. International Journal of Computer Sciences and Engineering, 7(6), 852-855.

BibTex Style Citation:
@article{Barik_2019,
author = {Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak},
title = {Transfer Learning:Approaches and Methodologies},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {6 2019},
volume = {7},
Issue = {6},
month = {6},
year = {2019},
issn = {2347-2693},
pages = {852-855},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4642},
doi = {https://doi.org/10.26438/ijcse/v7i6.852855}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i6.852855}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4642
TI - Transfer Learning:Approaches and Methodologies
T2 - International Journal of Computer Sciences and Engineering
AU - Swati Sucharita Barik, Mamata Garanayak, Sasmita Kumari Nayak
PY - 2019
DA - 2019/06/30
PB - IJCSE, Indore, INDIA
SP - 852-855
IS - 6
VL - 7
SN - 2347-2693
ER -

VIEWS PDF XML
527 313 downloads 126 downloads
  
  
           

Abstract

Machine learning and Data Mining Techniques are mainly used for many Real world problems. The traditional methods include training the data and test .But it will not be applicable for real world scenario. Some of the reason may be the cost of training data and inability to get those. These drawbacks are giving rise to the concept known as Transfer Learning.It ensures that training data must be independent and distributed identically.Transfer Learning is considered as a solution to the insufficient training data.

Key-Words / Index Term

Data Mining, Transfer Learning, Machine Learning

References

[1]Yosinski J,Clune J,Bengio Y and Lipson H,How transferable are features in deep neural networks?in Advances in Neural Information Processing Systems 27 (NIPS ’14), NIPS Foundation, 2014
[2] Dai, Wenyuan et al. “Translated Learning: Transfer Learning across Different Feature Spaces.” NIPS (2008).
[3]Mohsen Kaboli,”A Review of Transfer Learning Algorithms”,Technische Universität München, 2017
[4] Hinton G, Osindero S, Teh Y W. A Fast Learning Algorithm for Deep Belief Nets, J. Neural Computation, 2006, 18(7): 1527–1554
[5] Taghi M. Khoshgoftaar and DingDing Wang,” A survey of transfer learning”
[6] K. Driessens, J. Ramon, and T. Croonenborghs,”Transfer learning for reinforcement learning through goal and policy parametrization”.In ICML Workshop on Structural Knowledge Transfer for Machine Learning, 2006
[7] Rakesh Kumar Saini, "Data Mining tools and challenges for current market trends-A Review", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.2, pp.11-14, 2019
[8]Madhusmita Dey, Swati Sucharita Barik,”Security Enhancement in ATMs through Helmet Detection using Inductive Transfer Learning”,IJSRET,Volume 7, Issue 4, April 2018
[9]Sinno Jialin Pan and Qiang Yang.”A survey on transfer learning. IEEE Transactions on knowledge and data engineering”, 22(10):1345–1359, 2010
[10]Hands-On Machine Learning with Scikit-Learn and Tensor Flow: Concepts, Tools, and Techniques for Building Intelligent Systems
[11]Rich Caruana.”Multitask learning.Machine Learning”, 28(1):41–70, 1997
[12] Dai W, Yang Q, Xue GR, Yu Y (2007) Boosting for transfer learning. In: Proceedings of the 24th international conference on machine learning. p. 193–200.
[13]A. Quattoni, M. Collins, and T. Darrell.”Transfer learning for image classification with sparse prototype representations”,CVPR, 2008
[14]Trevor Hastie, Robert Tibshirani, and Jerome Friedman. “The Elements of Statistical Learning: Data Mining, Inference, and Prediction”. New York:Springer Verlag, 2001
[15]S. J. Pan, V. W. Zheng, Q. Yang, and D. H. Hu, “Transfer learning for wifi-based indoor localization,” in Proceedings of the Workshop on Transfer Learning for Complex Task of the 23rd AAAI Conference on Artificial Intelligence, Chicago, Illinois, USA, July 2008
[16]K. Nigam, A. K. McCallum, S. Thrun, and T. Mitchell, “Text classification from labelled and unlabeled documents using EM ” Machine Learning, vol. 39, no. 2-3, pp. 103–134, 2000
[17]L. I. Kuncheva and J. J. Rodrłguez, “Classifier ensembles with a random linear oracle,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 4, pp. 500–508, 2007
[18]X. Zhu, “Semi-supervised learning literature survey,” University of Wisconsin–Madison, Tech. Rep. 1530, 2006
[19]H. DaumeIII and D. Marcu, “Domain adaptation for statistical classifiers,” Journal of Artificial Intelligence Research, vol. 26, pp. 101–126, 2006
[20]T. Evgeniou and M. Pontil, “Regularized multi-task learning,” in Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Seattle, Washington, USA: ACM, August 2004, pp. 109–117
[21]J. Gao, W. Fan, J. Jiang, and J. Han, “Knowledge transfer via multiple model local structure mapping,” in Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Las Vegas, Nevada: ACM, August 2008, pp. 283–291
[22]L. Mihalkova and R. J. Mooney, “Transfer learning by mapping with minimal target data,” in Proceedings of the AAAI-2008 Workshop on Transfer Learning for Complex Tasks, Chicago, Illinois, USA, July 2008
[23]S. Bickel, M. Bruckner, and T. Scheffer, “Discriminative learning for ¨ differing training and test distributions,” in Proceedings of the 24th international conference on Machine learning. New York, NY, USA: ACM, 2007, pp. 81–88
[24]W. Dai, Y. Chen, G.-R. Xue, Q. Yang, and Y. Yu, “Translated learning,” in Proceedings of 21st Annual Conference on Neural Information Processing Systems, 2008
[25]S. Ramachandran and R. J. Mooney, “Theory refinement of Bayesian networks with hidden variables,” in Proceedings of the 14th International Conference on Machine Learning, Madison, Wisconson, USA, July 1998, pp. 454–462
[26]X. Wu, V. Kumar, J. R. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. J. McLachlan, A. F. M. Ng, B. Liu, P. S. Yu, Z.-H. Zhou, M. Steinbach, D. J. Hand, and D. Steinberg, “Top 10 algorithms in data mining,” Knowl. Inf. Syst., vol. 14, no. 1, pp. 1–37, 2008
[27] Zehai Gao, Cunbao Ma, Zhiyu She, Xu Dong, "An Enhanced Deep Extreme Learning Machine for Integrated Modular Avionics Health State Estimation", Access IEEE, vol. 6, pp. 65813-65823, 2018
[28] Huan Liu, Zheng Liu, Haobin Dong, Jian Ge, Zhiwen Yuan, Jun Zhu, Haiyang Zhang, Xuming Zeng, "Recurrent Neural Network-Based Approach for Sparse Geomagnetic Data Interpolation and Reconstruction", Access IEEE, vol. 7, pp. 33173-33179, 2019.
[29]Dhar Gupta K., Pampana R., Vilalta R., Ishida E. E. O. and de Souza R. S. 2016 IEEE Symposium on Computational Intelligence and Data Mining (CIDM-16) (Athens, Greece) Automated Supernova Ia Classification Using Adaptive Learning Techniques