A. Afzal, Chemical sensors : Comprehensive Sensor Techologies, 2011.

A. Arsat, 754-2008-ieee standard for floating-point arithmetic. Rapport technique, ANSI/IEEE. Cité page 65, Chemical Physics Letters, vol.467, issue.1, pp.344-347, 2008.

[. Ballantine, Acoustic wave sensors Theory, Design and Physico-Chemical Applications, pp.9-57, 1997.

D. Ballantine-et-wohltjen-;-ballantine and H. Et-wohltjen, Surface acoustic wave devices for chemical analysis, Analytical Chemistry, vol.61, issue.1, pp.704-715, 1989.

J. Barzilai and J. M. Et-borwein, Twopoint step size gradient methods, IMA Journal of Numerical Analysis, vol.8, issue.1, p.32, 1988.

[. Benedetti, Electronic nose and neural network use for the classification of honey, Apidologie, vol.35, issue.4, pp.1-6, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00891832

Y. Bengio, Foundations and trends in machine learning, 2009.

H. J. Bierens, The Nadaraya-Watson kernel regression function estimator, Topics in Advanced Econometrics, p.85, 1994.

C. M. Bishop, Neural networks for pattern recognition, p.30, 1995.

, Patter recognition and Machine Learning, p.25, 2006.

A. Blum-;-blum, Neural networks in C++ : an object-oriented framework for building connectionist systems, p.31, 1992.

. Bongrain, High sensitivity of diamond resonant microcantilevers for direct detection in liquids as probed by molecular electrostatic surface interactions, Langmuire, vol.27, issue.1, pp.12226-12234, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00740839

[. Boukherroub, Photochemical oxidation of hydrigenated boron-doped diamond surfaces, Electrochemistry Communications, vol.7, issue.1, pp.937-940, 2005.

[. Box, Time series analysis : forecasting and control, p.39, 2015.

. Boyd, S. Boyd, and L. Vandenberghe, Convex Optimization, 2014.

L. Breiman, Random forests, Machine learning, vol.45, issue.1, pp.38-127, 2001.

[. Breiman, Classification and regression trees, p.38, 1984.

. Brereton, R. G. Lloyd-;-brereton, and G. R. Lloyd, Partial least squares discriminant analysis : taking the magic away, Journal of Chemometrics, vol.28, issue.4, pp.213-225, 2014.

C. G. Broyden, The convergence of a class of doublerank minimization algorithms 1. general considerations, IMA Journal of Applied Mathematics, vol.6, issue.1, p.32, 1970.

[. Brudzewski, Classification of milk by means of an electronic nose and svm neural network, Sensors and Actuators B : Chemical, vol.98, issue.2, pp.291-298, 2004.

[. Buratti, Characterization and classification of italian barbera wines by using an electronic nose and an amperometric electronic tongue, Sensors and Actuators B : Chemical, vol.525, issue.1, pp.67-76, 2003.

R. A. Carmona-;-carmona, Time series models : Ar, ma, arma, and all that, Statistical Analysis of Financial Data in S-Plus, p.38, 2004.

[. Cevoli, Classification of pecorino cheeses using electronic nose combined with artificial neural network and comparison with gc-ms analysis of volatile compounds, Food Chemistry, vol.129, issue.1, pp.24-107, 2011.

T. H. Chein and Y. Tzeng, Cvd diamond grown by microwave plasma in mixtures of aceton/oxygen and acetone/carbon dioxide, Diamond and Related Materials, vol.8, issue.1, pp.1393-1401, 1999.

T. Chen and C. Et-guestrin, Xgboost : A scalable tree boosting system, Proceedings of the 2016 International Conference on Knowledge Discovery and Data Mining (KDD). Cité, p.127, 2016.

[. Chevallier, New sensitive coating based on modified diamond nanoparticles for chemical saw sensors, Sensors and Actuators B : Chemical, vol.154, issue.2, pp.238-244, 2011.

. Cho, J. H. Cho, and P. U. Et-kurup, Decision tree approach for classification and dimensionality reduction of electronic nose data, Sensors and Actuators B : Chemical, vol.160, issue.1, pp.542-548, 2011.

[. Chuanzhi, A novel toxic gases detection system based on saw resonator array and probabilistic neural network, In Electronic Measurement and Instruments, pp.499-503, 2007.

R. J. Clarke, Coffe Chemistry, p.106, 2013.

S. Derksen and H. J. Et-keselman, Backward, forward and stepwise automated subset selection algorithms : Frequency of obtaining authentic and noise variables, Bristish Journal of Mathematical and Statistical Psychology, vol.45, issue.1, pp.76-77, 1992.

. Dreyfus, Apprentissage statistique. Eyrolles, p.27, 2008.

[. Duval, An overview of signal processing issues in chemical sensing, Proceedings of the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00839421

R. A. Fisher and I. Flament, The use of multiple measurements in taxonomic problems, Annals of eugenics, vol.7, issue.2, p.106, 1936.

. Frenois, Detection of vapour explosives by a multi-sensor prototype-performance evaluation under laboratory and real conditions, Proceedings of the 2014 IEEE Sensors Symposium, p.40, 2014.

[. Fujioka, Discrimination method of the volatiles from fresh mushrooms by an electronic nose using a trapping system and statistical standardization to reduce sensor value variation, Sensors, vol.13, issue.11, pp.15532-15548, 2013.

. García, D. L. Aparicio-;-garcía, and R. Aparicio, Sensors : From biosensors to the electronic nose, Grasas y Aceites, vol.53, pp.96-114, 2002.

J. W. Gardner-;-gardner and . Gaudioso, On the use of the svm approach in analyzing an electronic nose, International Conference on Hybrid Intelligent Systems (HIS), vol.18, pp.42-46, 1994.

S. Geman and E. B. Doursat, Neural networks and the biais/variance dilemma, Neural computation, vol.4, issue.1, p.42, 1992.

[. Girard, Hydrogenation of nanodiamonds using mpcvd : A new route toward organic functionalization, Diamond and Related Materials, vol.19, issue.7, pp.1117-1123, 2010.
URL : https://hal.archives-ouvertes.fr/cea-01807231

[. Girard, Electrostatic grafting of diamond nanoparticles : a versatile route to nanocrystalline diamond thin films, ACS applied materials & interfaces, vol.1, issue.12, pp.2738-2746, 2009.
URL : https://hal.archives-ouvertes.fr/cea-01807224

F. Glover, . Güney, S. Atasoy-;-güney, A. Atasoy, A. Gogna et al., Multiclass classification of n-butanol concentrations with k-nearest neighbor algorithm and support vector machine in an electronic nose, Heuristics for integer programming using surrogate constraonts. Decision Sciences, vol.8, p.127, 1983.

J. Grate-;-grate, J. Grate, and R. Mcgill, Dewetting effects on polymer-coated surface acoustic wave vapor sensors, Analytical Chemistry, vol.100, issue.1, pp.4015-4019, 1995.

J. D. Hamilton-;-hamilton, Time series analysis, p.39, 1994.

P. C. Hansen-;-hansen and G. Harsanyi, The l-curve and its use in the numerical treatment of inverse problems. Rapport technique, Technical University of Denmark. Cité page 83, 1995.

[. Hietala, Dual saw sensor technique for determining mass and modulus changes, IEEE Transactions Ultrasonics Ferroelectrics Frequency Control, vol.49, issue.1, pp.262-266, 2001.

N. J. Higham-;-higham, Accuracy and stability of numerical algorithms, p.81, 1996.

. Lin, C. W. Hsu, C. J. Lin, and . Huang, Multiclass prediction with partial least square regression for gene expression data : applications in breast cancer intrinsic taxonomy, IEEE Transactions on Neural Networks, vol.13, p.26, 2002.

K. Ireland-et-rosen-;-ireland and M. Et-rosen, A Classical Introduction to Modern Number Theory, p.140, 1990.

R. A. Jacobs, Increased rates of convergence through learning rate adaptation, Neural Networks, vol.1, issue.4, pp.295-307, 1988.

J. Janata, Principles of Chemical Sensors, 2009.

S. K. Jha and R. D. Et-yadava, Preprocessing of saw sensor array data and pattern recognition, IEEE Sensors Journal, vol.9, issue.10, pp.1202-1208, 2009.

. Kennedy, J. Kennedy, and R. Eberhart, Particle swarm optimization, Neural Networks, vol.4, pp.1942-1948, 1995.

[. Kirkpatrick, Optimization by simulated annealing, Science, vol.220, issue.4598, p.135, 1983.

[. Kong, A qualitative analysis algorithm and its application in mixed gas identification, International Journal of Electronics and Electrical Engineering, vol.4, issue.1, p.107, 2016.

[. Kriegel, Outliers detection techniques, Proceedings of the 2010 SIAM International Conference on Data Mining (ICDM, p.90, 2010.

[. Lange, Surface acoustic wave biosensors : a review, Analytical and Bioanalytical Chemistry, vol.391, issue.1, pp.1509-1519, 2008.

. Leardi, R. Gonzales-;-leardi, and A. M. Gonzales, Genetic algorithms in variable selection. Rapport technique, University of Genoa, p.76, 1998.

W. S. Levine-;-levine, The Control Handbook, 1996.

[. Lili, Identification of early moldy rice samples by pca and pnn, Communications and Information Processing, vol.288, issue.1, pp.506-514, 2012.

[. Liu, A survey on gas sensing technology, Sensors, vol.12, issue.7, pp.9635-9665, 2012.

I. E. Livieris and P. Et-pintelas, A survey on algorithms for training artificial neural networks, p.32, 2008.

[. Llobet, Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thickfilm tin oxide gas sensor array, Sensors and Actuators B : Chemical, vol.41, issue.1, pp.13-21, 1997.

. Maddala, G. Maddala, and K. Lahiri, Introduction to econometrics, p.39, 2009.

R. Manai, Réseaux de biocapteurs de type MEMS en diamant pour la reconnaissance d'odeurs, pp.11-57, 2014.

[. Martinelli, Feature extraction of chemical sensors in phase space, Sensors and Actuators B : Chemical, vol.95, issue.1, pp.132-139, 2003.

[. Mayoue, Recursive least squares algorithm dedicated to early recognition of explosive compounds thanks to multi-technology sensors, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.13-39, 2013.
URL : https://hal.archives-ouvertes.fr/cea-01830772

. Meyer, A. R. Meyer, and R. Et-rubinfeld, Generating functions. Rapport technique, Massachusetts Institue of Technology, p.131, 2005.

B. Naidoo and A. D. Broadhurst, Sensor array data processing using a 2-d discrete cosine transform, International Symposium on Olfaction and Electronic Noses (ISOEN), p.23, 2000.

[. Olunloyo, Neural network based electronic nose for cocoa beans quality assessment, Agricultural Engineering Journal, vol.13, issue.4, p.30, 2011.

A. V. Oppenheim and R. W. Schafer, Digital Signal Processing, pp.3-129, 2009.

. Pardo, . Sberveglieri, M. Pardo, and G. Sberveglieri, Classification of electronic nose data with support vector machines, Sensors and Actuators B : Chemical, vol.107, issue.2, pp.730-737, 2005.

[. Penza, Layered saw gas sensor with single-walled carbon nanotube-based nanocomposite coating, Sensors and Actuators B : Chemical, vol.127, issue.1, pp.168-178, 2007.

K. B. Peterson and M. S. Et-pederson, The matrix cookbook. Rapport technique, Technical University of Denmark, 2012.

. Philip, J. Hess-;-philip, and P. Hess, Elastic, mechanical, and thermal properties of nanocrystalline diamond films, Journal of Applied Physics, vol.93, issue.1, pp.2164-2171, 2003.

A. P. Piotrowski and J. J. Et-napiorkowski, A comparison of methods to avoid overfitting in neural networks training in the case of catchment runoff modelling, Journal of Hydrology, vol.476, issue.1, pp.97-111, 2013.

. Plagianakos, Deterministic nonmonotone strategies for effective training of multilayer perceptrons, IEEE Transactions on, vol.13, issue.6, pp.1268-1284, 2002.

. Priddy, K. L. Keller-;-priddy, and P. E. Keller, Artificial Neural Networks : An Introduction, p.31, 2005.

J. R. Quinlan-;-quinlan, Induction of decision trees, Machine Learning, vol.1, p.37, 1986.

[. Raj, Investigations on the origin of mass and elastic loading in the time varying distinct response of zno saw ammonia sensor, Sensors and Actuators B : Chemical, vol.166, issue.1, pp.573-585, 2012.

C. R. Rao, The utilization of multiple measurements in problems of biological classification, Journal of the Royal Statistical Society. Series B (Methodological), vol.10, issue.2, pp.159-203, 1948.

[. Rapp, New miniaturized saw-sensor array for organic gas detection driven by multiplexed oscillators, Sensors and Actuators B : Chemical, vol.65, issue.1, pp.169-172, 2000.

R. Reed, Pruning algorithms-a survey, IEEE Transactions on, vol.4, issue.5, p.31, 1993.

R. T. Rockafellar, Lagrange multipliers and optimality, SIAM Journal on Control and Optimization, vol.35, issue.1, pp.183-238, 2006.

T. A. Wilson-;-roppel and D. Wilson, Improved chemical identification from sensor arrays using intelligent algorithms, Advanced Environmental and Chemical Sensing Technology, vol.4205, issue.1, p.30, 2001.

F. Rosenblatt, The perceptron : A probabilistic model for information storage and organization in the brain, Psychological Review, vol.65, issue.8, pp.386-408, 1958.

[. Rousier, Effectiveness of an electronic nose for monitoring bacterial and fungal growth, International Symposium on Olfaction and Electronic Noses (ISOEN), pp.173-180, 1995.

, Radial-basis-function networks : learning and applications

, An introduction to the conjugate gradient method without the agonizing pain, J. R, p.32, 1994.

P. Siarry-;-siarry, Métaheuristiques. Eyrolles, 1 édition. Cité pages 136, vol.137, 2014.

, Nitrogenation of boron doped siamond : Comparison of an electro-chemical treatment in liquid ammonia and a nh3/n2 plamsa, Diamond and Related Materials, vol.18, issue.1, pp.890-894, 2009.

[. Singh, Metal oxide saw e-nose employing pca and ann for theidentification of binary mixture of dmmp and methanol, Sensors and Actuators B : Chemical, vol.200, issue.1, p.107, 2014.

P. Singh and R. D. Et-yadava, Wavelet based fuzzy inference system for simultaneous identification and quatitation of volatile organic compounds using saw sensor transients, International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO), pp.319-327, 2011.

. Soh, , p.155, 2014.

D. F. Specht, sed electronic nose for herbs recognition, International Journal on Smart Sensing and Intelligent Systems, vol.7, issue.2, pp.109-118, 1990.

[. Srivastava, Dropout : A simple way to prevent neural networks from overfitting, The Journal of Machine Learning Research, vol.15, issue.1, pp.1929-1958, 2014.

K. O. Stanley, Evolving neural networks through augmenting topologies, Evolutionary Computation, vol.10, issue.2, pp.31-32, 2002.

P. Stoica and Y. Et-selen, Model-order selection : a review of information criterion rules, IEEE Signal Processing Magazine, vol.21, issue.4, p.39, 2004.

[. Strother, Photochemical functionalization of diamond films, Langmuir, vol.18, issue.24, pp.968-971, 2002.

[. Tang, Development of a portable electronic nose system for the detection and classification of fruity odors, Sensors, vol.10, issue.1, p.107, 2010.

B. Tard, Etudes des Interactions Gaz-Surfaces Diamant par Gravimétrie sur Résonateur à Onde Acoustique, Cité pages, vol.8, issue.11, p.57, 2013.

D. Vainbrand and R. Et-ginosar, Network-on-chip architectures for neural networks, Proceedings of the Fourth ACM/IEEE International Symposium on Networks-on-Chip (NOCS, p.40, 2010.

I. Voiculescu and A. Et-nordin, Acoustic wave based mems devices for biosensing application, Biosensors and Bioelectronics, vol.33, issue.1, pp.1-9, 2012.

[. Wallard, Tables of physical and chemical constants. Rapport technique, 2005.

[. Weinberger, Distance metric learning for large margin nearest neighbor classification, Advances in Neural Information Processing Systems, vol.18, pp.1473-1480, 2006.

[. Wen, Enhanced sensitivity of saw gas sensor coated molecularly imprinted polymer incorporating high frequency stability oscillator, Sensors and Actuactors B : Chemical, vol.125, pp.422-427, 2007.

. Zielinski, K. Zielinski, and R. Et-laur, Cité page 138. Table des figures 1.1 Principe de fonctionnement d'un capteur SAW de type résonateur à onde de Rayleigh, vol.31, pp.51-59, 2007.

, Image du banc de gaz utilisé au Laboratoire Capteurs Diamant, p.14

. , Réponse des 8 capteurs lors d'une exposition à 10 ppm de sulfure d'hydrogène

. , Réponse des 4 capteurs en présence d'une capsule de café authentique

. , Réponse des 8 capteurs en présence d'un mélange composé de DMMP et d'éthanol

, Répétabilité des acquisitions pour la base des toxiques chimiques (a) et (b), pour la base des capsules de café (c) et (d)

, Illustration des régimes transitoire et stationnaire de la réponse d'un capteur SAW exposé à 10 ppm d'ammoniac, p.22

, Exemple d'un autoencodeur permettant de reconstruire des données de dimension 4 avec 3 neurones dans la couche cachée, p.25

, Illustration de l'hyperplan séparateur optimal et des marges, p.27

, Représentation graphique d'un neurone symbolique, p.29

, Exemple d'un perceptron multicouches avec 4 neurones dans la couche d'entrée, deux couches cachées constituées de 6 et 3 neurones et 2 neurones dans la couche de sortie, p.30

. , Exemple d'un réseau RBF ayant 4 neurones dans la couche d'entrée, une couche radiale ayant 6 neurones, une couche de sortie de 3 neurones

, Exemple d'un réseau probabiliste ayant 4 neurones dans la couche d'entrée, une couche radiale ayant 6 neurones, une couche de sommation de 3 neurones et un 1 neurone de décision, p.35

, Interprétation géométrique de la différence entre KNN et LMNN, p.36

. , Exemple d'un arbre de décision et de ses frontières de classification

. .. , Influence de ? sur le taux de classification, p.122

, Précision, en échelle logarithmique, de l'estimation de la fonction génératrice associée à la séquence s, p.133

C. .. Exemples-de-topologie-de-voisinage, , vol.137, p.161

. , 78 5.7 Performances moyennes obtenues après sélection des descripteurs par l'heuristique de Hasse sur la base de données constituée du DMMP et du 4-NT lors d'un processus de validation croisée à 5 plis, Performances moyennes obtenues après sélection des descripteurs par l'heuristique de Hasse sur la base de données constituée des capsules de café lors d'un processus de validation croisée à 5 plis, p.86

, Fonctionnalisations sélectionnées pour différentes contraintes de coût sur la base de données constituée des toxiques chimiques, p.97

, Fonctionnalisations sélectionnées pour différentes contraintes de coût sur la base de données constituée des capsules de café, p.98

, Fonctionnalisations sélectionnées pour différentes contraintes de coût sur la base de données constituée du DMMP et du 4-NT, p.98

. , Typologie des problèmes et des approches pour l'identification de mélanges

. , Performances des algorithmes proposés dans le cas d'une base de données exhaustive

. , Performances moyennes obtenues lors d'un processus de validation croisée à 5 plis en utilisant les amplitudes en régime stationnaire et un SVM Gaussien

. , Performances moyennes obtenues lors d'un processus de validation croisée à 5 plis en utilisant les erreurs quadratiques moyennes et un SVM Gaussien

. , Analyse statistique du rapport signal sur bruit des signaux

, Performances moyennes obtenues lors d'un processus de validation croisée à 5 plis en utilisant les amplitudes en régime stationnaire et les erreurs quadratiques moyennes avec un autoencodeur et un ensemble d'arbres de décision, p.121

, Comparaison des performances moyennes lorsque N g est estimé

O. Hotel, J. P. Poli, C. Mer-calfati, E. Scorsone, and S. Saada, SAW sensor's frequency shift characterization for odour recognition and concentration estimation, IEEE Sensors, vol.17, issue.21, pp.7011-7018

O. Hotel, J. P. Poli, C. Mer-calfati, E. Scorsone, and S. Saada, A review of algorithms for SAW sensors e-nose based volatile compound identification, Sensors and Actuators B : Chemical, vol.255, issue.3, pp.2472-2482
URL : https://hal.archives-ouvertes.fr/hal-01879582

O. Hotel, J. P. Poli, C. Mer-calfati, E. Scorsone, and S. Saada, Estimation of the number of volatile compounds in simple mixture, Eurosensors, 2017.

O. Hotel, J. P. Poli, C. Mer-calfati, E. Scorsone, and S. Saada, Estimation of the parameters of SAW sensor's Frequency shift : application to odour recognition and concentration evaluation, Conférence ROADEF de la Société Française de Recherche Opérationnelle et Aide à la Décision, 2017.