Fuzzy Transfer Learning: Methodology and application

Information Sciences

Volumn 293, Issue , 2015, Pages 59-79

  Shell, Jethro ^a   Coupland, Simon ^a  

^a DE MONTFORT UNIVERSITY   (United Kingdom)

Author keywords

Context; Fuzzy logic; Intelligent environment; Online adaptation; Sensor network; Transfer learning

Indexed keywords

COMPUTER CIRCUITS; DATA ACQUISITION; FUZZY LOGIC; INTELLIGENT COMPUTING; PREDICTIVE ANALYTICS; SENSOR NETWORKS; TRANSFER LEARNING;

CONTEXT; CONTEXTUAL KNOWLEDGE; DYNAMIC ENVIRONMENTS; INTELLIGENT ENVIRONMENT; LEARNING METHODS; LEARNING TASKS; ON-LINE ADAPTATION; STANDARD MACHINES;

LEARNING SYSTEMS;

EID: 84922440761     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2014.09.004     Document Type: Article

References (64)

1. A. Arnold, R. Nallapati, and W.W. Cohen A comparative study of methods for transductive transfer learning Seventh IEEE International Conference on Data Mining Workshops, 2007. ICDM Workshops 2007 2007 IEEE 77 82
2. S. Barrett, M. Taylor, P. Stone, Transfer learning for reinforcement learning on a physical robot, in: Ninth International Conference on Autonomous Agents and Multiagent Systems-Adaptive Learning Agents Workshop (AAMAS-ALA), 2010.
3. C. Bettini, O. Brdiczka, K. Henricksen, J. Indulska, D. Nicklas, A. Ranganathan, and D. Riboni A survey of context modelling and reasoning techniques Pervas. Mob. Comput. 6 2 2010 161 180
4. H. Bou Ammar, E. Eaton, M.E. Taylor, D. Constantin Mocanu, K. Driessens, G. Weiss, K. Tuyls, An automated measure of MDP similarity for transfer in reinforcement learning, in: Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence, 2014.
5. J. Casillas, O. Cordón, F. Herrera, Improving the wang and mendel's fuzzy rule learning method by inducing cooperation among rules, in: Proceedings of the 8th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference, 2000, pp. 1682-1688.
6. G.C. Cawley, and N.L. Talbot On over-fitting in model selection and subsequent selection bias in performance evaluation J. Mach. Learn. Res. 11 2010 2079 2107
7. O. Chapelle, B. Schölkopf, and A. Zien Semi-Supervised Learning vol. 2 2006 MIT Press Cambridge, MA
8. D. Cook, K. Feuz, N. Krishnan, Transfer learning for activity recognition: a survey. < http://www.eecs.wsu.edu/cook/pubs/kais12.pdf >, 2012 (accessed 15.11.2012).
9. W. Dai, Y. Chen, G. Xue, Q. Yang, and Y. Yu Translated learning: transfer learning across different feature spaces NIPS 2008 2008 353 360
10. N.H. Dardas, and N.D. Georganas Real-time hand gesture detection and recognition using bag-of-features and support vector machine techniques IEEE Trans. Instrum. Meas. 60 11 2011 3592 3607
11. A. Dey Understanding and using context Personal Ubiquitous Comput. 5 1 2001 4 7
12. F. Doctor, H. Hagras, and V. Callaghan A fuzzy embedded agent-based approach for realizing ambient intelligence in intelligent inhabited environments IEEE Trans. Syst. Man Cybern., Part A: Syst. Humans 35 1 2005 55 65
13. P. Dourish What we talk about when we talk about context Personal Ubiquitous Comput. 8 1 2004 19 30
14. H. Drucker, C.J. Burges, L. Kaufman, A.J. Smola, and V. Vapnik Support vector regression machines Adv. Neural Inform. Process. Syst. 1996 155 161
15. K. Duan, S.S. Keerthi, and A.N. Poo Evaluation of simple performance measures for tuning svm hyperparameters Neurocomputing 51 2003 41 59
16. N. Gorski, J. Laird, Experiments in transfer across multiple learning mechanisms, in: Proceedings of the ICML-06 Workshop on Structural Knowledge Transfer for Machine Learning, 2006.
17. M. Grimm, K. Kroschel, and S. Narayanan Support vector regression for automatic recognition of spontaneous emotions in speech IEEE International Conference on Acoustics, Speech and Signal Processing, 2007 ICASSP 2007 vol. 4 2007 IEEE IV-1085
18. R. Gudwin, F. Gomide, Context adaptation in fuzzy processing.
19. S. Guillaume Designing fuzzy inference systems from data: an interpretability-oriented review IEEE Trans. Fuzzy Syst. 9 3 2001 426 443
20. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach. Learn. 46 1-3 2002 389 422
21. D. Hu, Q. Yang, Transfer learning for activity recognition via sensor mapping, in: Twenty-Second International Joint Conference on Artificial Intelligence, 2011, pp. 1962-1967.
22. S. Jang, Implementation of Context-Aware Application Model in Smart Environments, PhD thesis, Gwangju Institute of Science and Technology.
23. T. Joachims Text Categorization with Support Vector Machines: Learning with Many Relevant Features 1998 Springer
24. M. Kohli, G.D. Magoulas, and M.S. Thomas Transfer learning across heterogeneous tasks using behavioural genetic principles 13th UK Workshop on Computational Intelligence (UKCI), 2013 2013 IEEE 151 158
25. H. Larochelle, D. Erhan, Y. Bengio, Zero-data learning of new tasks, in: AAAI Conference on Artificial Intelligence, vol. 1, 2008, pp. 2-2.
26. Y. Li, S. Gong, and H. Liddell Support vector regression and classification based multi-view face detection and recognition Proceedings. Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000 2000 IEEE 300 305
27. P. Luo, F. Zhuang, H. Xiong, Y. Xiong, and Q. He Transfer learning from multiple source domains via consensus regularization Proceedings of the 17th ACM Conference on Information and Knowledge Management CIKM '08 2008 ACM New York, NY, USA 103 112
28. S. Madden, Intel lab data. < http://db.csail.mit.edu/labdata/labdata.html >, June 2004 (published 2.6.2004).
29. M. Maltamo, and A. Kangas Methods based on k-nearest neighbor regression in the prediction of basal area diameter distribution Can. J. For. Res. 28 8 1998 1107 1115
30. R. Martínez-Soto, O. Castillo, L.T. Aguilar, A. Rodriguez, A hybrid optimization method with PSO and GA to automatically design type-1 and type-2 fuzzy logic controllers, Int. J. of Mach. Learn. Cybern., April 2013, pp. 1-22.
31. J. Mendel Fuzzy logic systems for engineering: a tutorial Proc. IEEE 83 3 1995 345 377
32. J. Mendel Uncertainty, fuzzy logic, and signal processing Signal Process. 80 6 2000 913 933
33. R.S. Michalski A theory and methodology of inductive learning Artif. Intell. 20 2 1983 111 161
34. L. Mihalkova, T. Huynh, R. Mooney, Mapping and revising markov logic networks for transfer learning, in: Proceedings of the National Conference on Artificial Intelligence, vol. 22, 2007, p. 608.
35. E. Miller, Learning from One Example in Machine Vision by Sharing Probability Densities, Ph.D. Thesis, Yale University, 2002.
36. R. Moore Methods and Applications of Interval Analysis vol. 2 1987 Society for Industrial Mathematics Philadelophia
37. S. Pan, and Q. Yang A survey on transfer learning IEEE Trans. Knowl. Data Eng. 2009 1345 1359
38. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay Scikit-learn: machine learning in Python J. Mach. Learn. Res. 12 2011 2825 2830
39. W. Pedrycz Granular Computing: Analysis and Design of Intelligent Systems 2013 CRC Press
40. W. Pedrycz, R. Gudwin, and F. Gomide Nonlinear context adaptation in the calibration of fuzzy sets Fuzzy Sets Syst. 88 1 1997 91 97
41. D. Perkins, and G. Salomon Transfer of learning Int. Encyclopedia Educ. 2 1992 1
42. A. Quattoni, M. Collins, and T. Darrell Transfer learning for image classification with sparse prototype representations IEEE Conference on Computer Vision and Pattern Recognition, 2008 CVPR 2008 2008 IEEE 1 8
43. B. Schilit, N. Adams, R. Want, Context-aware computing applications, in: Proceedings of the 1994 First Workshop on Mobile Computing Systems and Applications, vol. 00, 1994, pp. 85-90.
44. A. Sengupta, and T. Pal On comparing interval numbers Eur. J. Oper. Res. 127 1 2000 28 43
45. M. Sharma, M. Holmes, J. Santamaria, A. Irani, C. Isbell, A. Ram, Transfer learning in real-time strategy games using hybrid CBR/RL, in: Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, No. 1041-1046, 2007.
46. J. Shell, S. Coupland, Improved decision making using fuzzy temporal relationships within intelligent assisted living environments, in: 2011 7th International Conference on Intelligent Environments (IE), 2011, pp. 149-156.
47. J. Shell, and S. Coupland Towards fuzzy transfer learning for intelligent environments Ambient Intell. 2012 145 160
48. J. Shell, S. Vickers, S. Coupland, H. Istance, Towards dynamic accessibility through soft gaze gesture recognition, in: 2012 12th UK Workshop on Computational Intelligence (UKCI), 2012, pp. 1-8.
49. A.J. Smola, and B. Schölkopf A tutorial on support vector regression Stat. Comput. 14 3 2004 199 222
50. K. Stensbo-Smidt, C. Igel, A. Zirm, and K.S. Pedersen Nearest neighbour regression outperforms model-based prediction of specific star formation rate IEEE International Conference on Big Data, 2013 2013 IEEE 141 144
51. D. Teodorovic, P. Lucic, J. Popovic, S. Kikuchi, B. Stanic, Intelligent isolated intersection, in: The 10th IEEE International Conference on Fuzzy Systems, 2001. vol. 1, 2001, pp. 276-279.
52. S. Thrun Is learning the n-th thing any easier than learning the first? Adv. Neural Inform. Process. Syst. 1996 640 646
53. L. Torrey, and J. Shavlik Transfer learning Handbook Res. Mach. Learn. Appl. IGI Global 3 2009 17 35
54. T.B. Trafalis, and H. Ince Support vector machine for regression and applications to financial forecasting IEEE-INNS-ENNS International Joint Conference on Neural Networks vol. 6 2000 IEEE Computer Society pp. 348-353
55. L. Wang, and J. Mendel Generating fuzzy rules by learning from examples IEEE Trans. Syst. Man Cybern. 22 6 1992 1414 1427
56. X. Wang, and J. Hong Learning optimization in simplifying fuzzy rules Fuzzy Sets Syst. 106 3 1999 349 356
57. X. Wang, Y. Wang, X. Xu, W. Ling, and D.S. Yeung A new approach to fuzzy rule generation: fuzzy extension matrix Fuzzy Sets Syst. 123 3 2001 291 306
58. G. Werner-Allen, K. Lorincz, M. Ruiz, O. Marcillo, J. Johnson, J. Lees, and M. Welsh Deploying a wireless sensor network on an active volcano IEEE Int. Comput. 10 2 2006 18 25
59. Q. Xu, and Q. Yang A survey of transfer and multitask learning in bioinformatics J. Comput. Sci. Eng. 5 3 2011 257 268
60. X. Yang, J. Yuan, J. Yuan, and H. Mao An improved WM method based on PSO for electric load forecasting Expert Syst. Appl. 37 12 2010 8036 8041
61. Y. Yao, G. Doretto, Boosting for transfer learning with multiple sources, in: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 1855-1862.
62. L. Zadeh Fuzzy logic Computer 21 4 1988 83 93
63. L. Zadeh Soft computing and fuzzy logic IEEE Softw. 11 6 1994 48 56
64. G. Zussman, and A. Segall Energy efficient routing in ad hoc disaster recovery networks Ad Hoc Netw. 1 4 2003 405 421