L. Hsuan, -. Tien, and L. Chih-jen, A Study on Sigmoid Kernels for SVM and the Training of non-PSD Kernels by SMO-type Methods, 2003.

Y. Chen and M. R. Gupta, Fusing similarities and kernels for classification, Information Fusion, 2009. FUSION'09. 12th International Conference on, pp.474-481, 2009.

G. Loosli, Study on the loss of information caused by the "positivation" of graph kernels for 3d shapes, 24th European Symposium on Artificial Neural Networks Bruges, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01593564

. Cheng-soon, X. Ong, S. Mary, A. J. Canu, and . Smola, Learning with non-positive kernels, ICML '04: Proceedings of the twenty-first international conference on Machine learning, vol.81, 2004.

G. Loosli, S. Canu, and C. Ong, Learning SVM in Kre? ?n Spaces. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.38, pp.1204-1216, 2016.
DOI : 10.1109/tpami.2015.2477830

URL : https://hal.archives-ouvertes.fr/hal-01593553/file/learningKrein.pdf

T. Ya, I. S. Azizov, and . Iokhvidov, Linear Operators in Spaces with an Indefinite Metric, 1989.

H. Xu, H. Xue, X. Chen, and Y. Wang, Solving Indefinite Kernel Support Vector Machine with Difference of Convex Functions Programming, AAAI, pp.2782-2788, 2017.

X. Huang, A. Maier, J. Hornegger, and J. Suykens, Indefinite kernels in least squares support vector machines and principal component analysis. Applied and Computational Harmonic Analysis, vol.43, pp.162-172, 2017.

X. Siamak-mehrkanoon, J. A. Huang, and . Suykens, Indefinite kernel spectral learning, Pattern Recognition, vol.78, pp.144-153, 2018.

M. Frank-, P. Schleif, and . Tino, Indefinite Core Vector Machine, Pattern Recognition, vol.71, pp.187-195, 2017.

F. Schleif, C. Raab, and P. Tino, Sparsification of Indefinite Learning Models, Structural, Syntactic, and Statistical Pattern Recognition, pp.173-183, 2018.
DOI : 10.1007/978-3-319-97785-0_17

S. V. Vishwanathan, A. J. Smola, M. Narasimha-murty, and . Simplesvm, Proceedings of the Twentieth International Conference on International Conference on Machine Learning, ICML'03, pp.760-767, 2003.