참고문헌 (36)

1. Gardner J , Bartlett P . Electronic noses, principles and applications . New York : Oxford University Press ; 1999 . 

2. Pearce TC , Schiffman SS , Nagle HT , Gardner JW . Handbook of machine olfaction: electronic nose technology . Weinheim, Germany : Wiley‐VCH ; 2003 . pp？ 134 – 159 . 

3. Gutierrez‐Osuna R . Pattern analysis for machine olfaction: a review . IEEE Sensors J 2002 ; 2 ( 3 ): 189 – 202 . 

4. Klinkenberg R , Joachims T . Detecting concept drift with support vector machines . In: Proc 17th Int Conf Machine Learning (ICML 2000), Stanford University, Stanford, CA; June 29–July 2, 2000 . pp？ 487 – 494 . 

5. Vergaraa A , Vembua S , Ayhanb T , Vitae MAR , Vitae MLH , Huerta R . Chemical gas sensor drift compensation using classifier ensembles . Sens Actuators B 2012 ; 166–167 : 320 – 329 . 

6. Romain A‐C , Nicolas J . Long term stability of metal oxide‐based gas sensors for e‐nose environmental applications: an overview . Sens Actuators B 2010 ; 146 ( 2 ): 502 – 506 . 

7. Göpel W . New materials and transducers for chemical sensors . Sens Actuators B 1994 ; 18 ( 1–3 ): 1 – 21 . 

8. Yamazoe N . New approaches for improving semiconductor gas sensors . Sens Actuators B 1991 ; 5 ( 1–4 ): 7 – 19 . 

9. Roth M , Hartinger R , Faul R , Endres H‐E . Drift reduction of organic coated gas‐sensors by temperature modulation . Sens Actuators B 1996 ; 36 ( 1–3 ): 358 – 362 . 

10. Aigner R , Auerbach F , Huber P , Müller R , Scheller G . Sinusoidal temperature modulation of the Si‐planar‐Pellistor . Sens Actuators B 1994 ; 18 ( 1–3 ): 143 – 147 . 

11. Gutierrez‐Osuna R . Drift reduction for metal‐oxide sensor arrays using canonical correlation regression and partial least squares , In: Proc 7th Int Symp Olfaction and Electronic Nose, Brighton, UK; July 20–24, 2000 . 

12. Artursson T , Eklöv T , Lundström I , Mårtensson P , Sjöström M , Holmberg M . Drift correction for gas sensors using multivariate methods . J Chemomet 2000 ; 14 ( 5–6 ): 711 – 723 . 

13. Haugen JE , Tomic O , Kvaal K . A calibration method for handling the temporal drift of solid‐state gas sensors . Anal Chim Acta 2000 ; 407 ( 1–2 ): 23 – 39 . 

14. Vito SD , Fattoruso G , Pardo M , Tortorella F , Francia GD . Semi Supervised learning techniques in artificial olfaction: a novel approach to classification problems and drift counteraction . IEEE Sensors J 2012 ; 12 ( 11 ): 3215 – 3223 . 

15. Liu Q , Li X , Ye M , Ge SS , Du X . Drift compensation for electronic nose by semi‐supervised domain adaptation . IEEE Sensors J 2014 ; 14 ( 3 ): 657 ‐ 665 . 

16. Längkvist M , Loutfi A . Unsupervised feature learning for electronic nose data applied to Bacteria Identification in Blood . In: Proc NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, Granada, Spain; December 12–14, 2011 . 

17. Trincavelli M , Coradeschi S , Loutfi A , Söderquist B , Thunberg P . Direct identification of bacteria in blood culture samples using an electronic nose . IEEE Trans Biomed Eng 2010 ; 57 ( 12 ): 2884 – 2890 . 

    상세보기

18. Bruins M , Bos A , Petit PL , Eadie K , Rog A , Bos R , van Ramshorst GH , van Belkum A . Device‐independent, real‐time identification of bacterial pathogens with a metal oxide‐based olfactory sensor . Eur J Clin Microbiol Infect Dis 2009 ; 28 ( 7 ): 775 – 780 . 

    상세보기

19. Llobet E , Brezmes J , Vilanova X , Sueiras JE , Correig X . Qualitative and quantitative analysis of volatile organic compounds using transient and steady‐state responses of a thick‐film tin oxide gas sensor array . Sens Actuators B 1997 ; 41 ( 1–3 ): 13 – 21 . 

20. Muezzinoglu MK , Vergara A , Huerta R , Rulkov N , Rabinovich MI , Selverston A , Abarbanel HD . Acceleration of chemo‐sensory information processing using transient features . Sens Actuators B 2009 ; 137 ( 2 ): 507 – 512 . 

21. Hinton GE , Osindero S , Teh Y . A fast learning algorithm for deep belief nets . Neural Comput 2006 ; 18 : 1527 – 1554 . 

    상세보기

22. Bengio Y , Lamblin P , Popovici D , Larochelle H . Greedy layer‐wise training of deep net‐works . Adv Neural Inform Process Syst 2006 ; 19 : 153 – 160 . 

23. Coates A , Carpenter B , Case C , Satheesh S , Suresh B , Wang T , Ng AY . Text detection and character recognition in scene images with unsupervised feature learning . In: 2011 Int Conf on Document Analysis and Recognition, ICDAR 2011, Beijing, China; September 18–21, 2011 . pp？ 440 – 445 . 

24. Jaitly N , Hinton GE . Learning a better representation of speech sound waves using restricted Boltzmann machines . In: Proc IEEE Int Conf Acoustics, Speech, and Signal Processing, ICASSP 2011, Prague Congress Center, Prague, Czech Republic.; May 22–27, 2011 . pp？ 5884 – 5887 . 

25. Lee H , Largman Y , Pham P , Ng AY . Unsupervised feature learning for audio classification using convolutional deep belief networks . In: Advances in Neural Information Processing Systems 22 (NIPS 2009), Vancouver, British Columbia, Canada; December 7–10, 2009 . pp？ 1096 – 1104 . 

26. Taylor G , Hinton GE , Roweis S . Modeling human motion using binary latent variables . In: Advances in Neural Information Processing Systems 19, Vancouver, British Columbia, Canada; December 4–7, 2006 . pp？ 1345 – 1352 . 

27. Wulsin D , Gupta J , Mani R , Blanco J , Litt B . Modeling electroencephalography waveforms with semi‐supervised deep belief nets: faster classification and anomaly measurement . J Neural Eng 2011 ; 8 : 1741 – 2552 . 

28. Nair V , Hinton GE . 3 D object recognition with deep belief nets . In: Advances in Neural Information Processing Systems 19, Vancouver, British Columbia, Canada; December 4–7, 2006 . pp？ 1339 – 1447 . 

29. Le QV , Zou WY , Yeung SY , Ng AY . Learning hierarchical invariant spatial‐temporal features for action recognition with independent subspace analysis . In: The 24th IEEE Conf Computer Vision and Pattern Recognition, CVPR 2011, Colorado Springs, CO; June 20–25, 2011 . pp？ 3361 – 3368 . 

30. Hinton GE . Training products of experts by minimizing contrastive divergence . Neural Comput 2002 ; 14 : 1771 – 1800 . 

    상세보기

31. Le QV , Ngiam J , Coates A , Lahiri A , Prochnow B , Ng AY . On optimization methods for deep learning . In: Proc 28th Int Conf Machine Learning, ICML 2011, Bellevue, Washington, USA; June 28–July 2, 2011 . pp？ 265 – 272 . 

32. Rumelhart DE , Hinton GE , Williams RJ . Learning representations by back‐propagating errors . Nature 1986 ; 323 : 533 – 536 . 

33. Data set available at http://archive.ics.uci.edu/ml/datasets/Gas+Sensor+Array+Drift+Dataset, accessed 25 April, 2012. 

34. Cortes C , Vapnik V . Support‐vector networks . Mach Learn 1995 ; 20 ( 3 ): 273 – 297 . 

    상세보기

35. Chang C‐C , Lin C‐J . LIBSVM: a library for support vector machines , Software available at http://www.csie.ntu.edu.tw/∼cjlin/libsvm/, accessed November 15, 2014 . 

36. Hinton GE . A practical guide to training restricted Boltzmann machines . Momentum 2010 ; 9 ( 1 ): 3 – 20 .