D. Haussler, Convolution kernels on discrete structures University of California at Santa Cruz, 1999.

B. Haasdonk and C. Bahlmann, Learning with distance substitution kernels, " in Pattern Recognition, ser. Lecture Notes in Computer Science, pp.220-227, 2004.

J. P. Vert, H. Saigo, and T. Akutsu, Local alignment kernels for biological sequences, Kernel Methods in Computational Biology, pp.131-154, 2004.

C. Cortes, P. Haffner, and M. Mohri, Rational kernels: Theory and algorithms, J. Mach. Learn. Res, vol.5, pp.1035-1062, 2004.

M. Cuturi, J. Vert, T. Birkenes, and . Matsui, A Kernel for Time Series Based on Global Alignments, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '07, pp.413-416, 2007.
DOI : 10.1109/ICASSP.2007.366260

V. I. Levenshtein, Binary codes capable of correcting deletions , insertions, and reversals, english translation in Soviet Physics Doklady, pp.845-848, 1965.

R. A. Wagner and M. J. Fischer, The String-to-String Correction Problem, Journal of the ACM, vol.21, issue.1, pp.168-173, 1973.
DOI : 10.1145/321796.321811

T. Smith and M. Waterman, Identification of common molecular subsequences, Journal of Molecular Biology, vol.147, issue.1, pp.195-197, 1981.
DOI : 10.1016/0022-2836(81)90087-5

S. Altschul, W. Gish, W. Miller, E. Myers, and D. Lipman, Basic local alignment search tool, Journal of Molecular Biology, vol.215, issue.3, pp.403-410, 1990.
DOI : 10.1016/S0022-2836(05)80360-2

W. Pearson, [5] Rapid and sensitive sequence comparison with FASTP and FASTA, Methods Enzymol, vol.183, pp.63-98, 1990.
DOI : 10.1016/0076-6879(90)83007-V

V. M. Velichko and N. G. Zagoruyko, Automatic recognition of 200 words, International Journal of Man-Machine Studies, vol.2, issue.3, pp.223-234, 1970.
DOI : 10.1016/S0020-7373(70)80008-6

H. Sakoe and S. Chiba, A dynamic programming approach to continuous speech recognition, Proceedings of the Seventh International Congress on Acoustics, pp.65-69, 1971.

L. Chen and R. Ng, On The Marriage of Lp-norms and Edit Distance, Proceedings of the Thirtieth International Conference on Very Large Data Bases - ser. VLDB '04. VLDB Endowment, pp.792-803, 2004.
DOI : 10.1016/B978-012088469-8.50070-X

P. Marteau, Time warp edit distance with stiffness adjustment for time series matching Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.31, issue.2, pp.306-318, 2009.

A. Hayashi, Y. Mizuhara, and N. Suematsu, Embedding Time Series Data for Classification, Lecture Notes in Computer Science, vol.3587, pp.356-365, 2005.
DOI : 10.1007/11510888_35

B. Haasdonk, Feature space interpretation of svms with indefinite kernels Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol.27, issue.4, pp.482-492, 2005.

V. Vapnik, Statistical Learning Theory, 1989.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

B. Scholkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, 2001.

D. Zhang, W. Zuo, D. Zhang, and H. Zhang, Time Series Classification Using Support Vector Machine with Gaussian Elastic Metric Kernel, 2010 20th International Conference on Pattern Recognition, pp.29-32, 2010.
DOI : 10.1109/ICPR.2010.16

A. Woznica, A. Kalousis, and M. Hilario, Distances and (Indefinite) Kernels for Sets of Objects, Sixth International Conference on Data Mining (ICDM'06), pp.1151-1156, 2006.
DOI : 10.1109/ICDM.2006.60

G. Wu, E. Y. Chang, and Z. Zhang, Learning with non-metric proximity matrices, Proceedings of the 13th annual ACM international conference on Multimedia , MULTIMEDIA '05, pp.411-414, 2005.
DOI : 10.1145/1101149.1101239

Y. Chen, E. K. Garcia, M. R. Gupta, A. Rahimi, and L. Cazzanti, Similarity-based classification: Concepts and algorithms, J. Mach. Learn. Res, vol.10, pp.747-776, 2009.

K. Sivaramakrishnan and C. Bhattacharyya, Time Series Classification for Online Tamil Handwritten Character Recognition ??? A Kernel Based Approach, Neural Information Processing, ser, pp.800-805, 2004.
DOI : 10.1007/978-3-540-30499-9_123

K. Kumara, R. Agrawal, and C. Bhattacharyya, A large margin approach for writer independent online handwriting classification, Pattern Recognition Letters, vol.29, issue.7, pp.933-937, 2008.
DOI : 10.1016/j.patrec.2008.01.016

H. Saigo, J. Vert, N. Ueda, and T. Akutsu, Protein homology detection using string alignment kernels, Bioinformatics, vol.20, issue.11, pp.1682-1689, 2004.
DOI : 10.1093/bioinformatics/bth141

URL : https://hal.archives-ouvertes.fr/hal-00433587

C. Berg, J. P. Christensen, and P. , Harmonic Analysis on Semigroups: Theory of Positive Definite and Related Functions, ser. Graduate Texts in Mathematics, 1984.
DOI : 10.1007/978-1-4612-1128-0

J. Shawe-taylor and N. Cristianini, Kernel Methods for Pattern Analysis, 2004.
DOI : 10.1017/CBO9780511809682

P. F. Marteau, Time warp edit distance VALORIA, Tech. Rep, 2008.
DOI : 10.1109/tpami.2008.76

URL : http://arxiv.org/abs/cs/0703033

K. Shin and T. Kuboyama, A Generalization of Haussler's Convolution Kernel ??? Mapping Kernel and Its Application to Tree Kernels, Journal of Computer Science and Technology, vol.2, issue.6, pp.1040-1054, 2010.
DOI : 10.1007/s11390-010-9386-1

I. J. Schoenberg, Metric spaces and positive definite functions, Transactions of the American Mathematical Society, vol.44, issue.3, pp.522-536, 1938.
DOI : 10.1090/S0002-9947-1938-1501980-0

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.377.3750

C. Micchelli, Interpolation of scattered data: Distance matrices and conditionally positive definite functions, Constructive Approximation, vol.23, issue.1, pp.11-22, 1986.
DOI : 10.1007/BF01893414

J. C. Platt, Advances in kernel methods, ch. Fast Training of Support Vector Machines Using Sequential Minimal Optimization, pp.185-208, 1999.

P. Chen, R. Fan, and C. Lin, A Study on SMO-Type Decomposition Methods for Support Vector Machines, IEEE Transactions on Neural Networks, vol.17, issue.4, pp.893-908, 2006.
DOI : 10.1109/TNN.2006.875973

E. J. Keogh, X. Xi, L. Wei, and C. Ratanamahatana, The UCR time series classification-clustering datasets, 2006.

V. Vapnik, The Nature of Statistical Learning Theory, 1995.

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, pp.1-27, 2011.
DOI : 10.1145/1961189.1961199

C. A. Ratanamahatana and E. J. Keogh, Making Time-series Classification More Accurate Using Learned Constraints, Proceedings of the Fourth SIAM International Conference on Data Mining (SDM'04), pp.11-22, 2004.
DOI : 10.1137/1.9781611972740.2

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.134.2280

P. Marteau and S. Gibet, Down-sampling Coupled to Elastic Kernel Machines for Efficient Recognition of Isolated Gestures, 2014 22nd International Conference on Pattern Recognition, 2014.
DOI : 10.1109/ICPR.2014.71

URL : https://hal.archives-ouvertes.fr/hal-00995279

C. Cortes, P. Haffner, and M. Mohri, Positive Definite Rational Kernels, Lecture Notes in Computer Science, vol.2777, pp.41-56, 2003.
DOI : 10.1007/978-3-540-45167-9_5

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.111.7226