Deep convolutional neural networks for Raman spectrum recognition: a unified solution

Lieber, 2003, Appl. Spectrosc., 57, 1363, 10.1366/000370203322554518

Kneen, 1996, Nucl. Instrum. Methods Phys. Res., Sect. B, 109, 209, 10.1016/0168-583X(95)00908-6

S. Wartewig , IR and Raman Spectroscopy: Fundamental Processing, Wiley-VCH Verlag GmbH & Co. KGaA, 2005, pp. 75–124

Zhang, 2010, Analyst, 135, 1138, 10.1039/b922045c

Baek, 2015, Analyst, 140, 250, 10.1039/C4AN01061B

P. H. C. Eilers and H. F.Boelens, Baseline Correction with Asymmetric Least Squares Smoothing, Leiden university medical centre technical report, 2005

Schulze, 2005, Appl. Spectrosc., 59, 545, 10.1366/0003702053945985

V. Vapnik , The Nature of Statistical Learning Theory, in Data mining and knowledge discovery, 1995

Sattlecker, 2010, Analyst, 135, 895, 10.1039/b920229c

Widjaja, 2008, Int. J. Oncol., 32, 653

A. Kyriakides , E.Kastanos and C.Pitris, Classification of Raman Spectra using Support Vector Machines, in 2009 9th International Conference on Information Technology and Applications in Biomedicine, 2009, pp. 1–4

Ho, 1998, IEEE Trans. Pattern Analysis Machine Intelligence, 20, 832, 10.1109/34.709601

Maguire, 2015, Analyst, 140, 2473, 10.1039/C4AN01887G

Maquelin, 2003, J. Clin. Microbiol., 41, 324, 10.1128/JCM.41.1.324-329.2003

Ishikawa, 2013, Comput. Geosci., 54, 259, 10.1016/j.cageo.2013.01.011

Carey, 2015, J. Raman Spectrosc., 46, 894, 10.1002/jrs.4757

B. Lafuente , R. T.Downs, H.Yang and N.Stone, The power of databases: the RRUFF project, in Highlights in Mineralogical Crystallography, 2015, pp. 1–30

Kwiatkowski, 2010, Metrol. Meas. Syst., 17, 549, 10.2478/v10178-010-0045-1

Hubel, 1968, J. Physiol., 195, 215, 10.1113/jphysiol.1968.sp008455

LeCun, 1998, Proc. IEEE, 86, 2278, 10.1109/5.726791

Lecun, 1998, Proc. IEEE, 2278, 10.1109/5.726791

C. Szegedy , W.Liu, Y.Jia, P.Sermanet, S.Reed, D.Anguelov, D.Erhan, V.Vanhoucke and A.Rabinovich, Computer Vision and Pattern Recognition (CVPR), 2015

K. He , X.Zhang, S.Ren and J.Sun, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 770–778

A. L. Maas , A. Y.Hannun and A. Y.Ng, Proc. ICML, 2013

S. Ioffe and C.Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, ArXiv e-prints, 2015

Srivastava, 2014, J. Mach. Learn. Res., 15, 1929

D. Kingma and J.Ba, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, 2014

C. M. Bishop , Pattern Recognition and Machine Learning (Information Science and Statistics), 2006

F. Chollet , et al., Keras, 2015, https://github.com/fchollet/keras

M. Abadi , A.Agarwal, P.Barham, E.Brevdo, Z.Chen, C.Citro, G. S.Corrado, A.Davis, J.Dean, M.Devin, S.Ghemawat, I.Goodfellow, A.Harp, G.Irving, M.Isard, Y.Jia, R.Jozefowicz, L.Kaiser, M.Kudlur, J.Levenberg, D.Mané, R.Monga, S.Moore, D.Murray, C.Olah, M.Schuster, J.Shlens, B.Steiner, I.Sutskever, K.Talwar, P.Tucker, V.Vanhoucke, V.Vasudevan, F.Viégas, O.Vinyals, P.Warden, M.Wattenberg, M.Wicke, Y.Yu and X.Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems, 2015, Software available from tensorflow.org

L. Buitinck , G.Louppe, M.Blondel, F.Pedregosa, A.Mueller, O.Grisel, V.Niculae, P.Prettenhofer, A.Gramfort, J.Grobler, R.Layton, J.VanderPlas, A.Joly, B.Holt and G.Varoquaux, ECML PKDD Workshop: Languages for Data Mining and Machine Learning, 2013, pp. 108–122

A. Krizhevsky , I.Sutskever and G. E.Hinton, Advances in neural information processing systems, 2012, pp. 1097–1105

Ruckstuhl, 2001, J. Quant. Spectrosc. Radiat. Transfer, 68, 179, 10.1016/S0022-4073(00)00021-2

DiFeST , http://www.fis.unipr.it/phevix/ramandb.php