Journal of Mathematical Sciences: Advances and Applications[1] Andrzej Cichocki, Sergio Cruces and Shun-ichi Amari,
Log-determinant revisited: Alpha-beta and gamma log-det divergences,
Entropy 17(5) (2015), 2988-3034.
DOI: https://doi.org/10.3390/e17052988
[2] Andrzej Cichocki, Sergio Cruces and Shun-ichi Amari, Generalized
alpha-beta divergences and their application to robust nonnegative
matrix factorization, Entropy 13(1) (2011), 134-170.
DOI: https://doi.org/10.3390/e13010134
[3] Asa Ben-Hur and Willian Stafford Noble, Kernel methods for
predicting protein-protein-interactions, Bioinformatics 21(1) (2005),
i38-i46.
DOI: https://doi.org/10.1093/bioinformatics/bti1016
[4] B. Frénay and M. Verleysen, Parameter-insensitive kernel in
extreme learning for nonlinear support vector regression,
Neurocomputing 74(16) (2011), 2526-2531.
DOI: https://doi.org/10.1016/j.neucom.2010.11.037
[5] B. Fuglede, Spirals in Hilbert space: With an application in
information theory, Expositiones Mathematicae 23(1) (2005), 23-45.
DOI: https://doi.org/10.1016/j.exmath.2005.01.014
[6] B. Schölkopf and A. Smola, Learning with Kernels, MIT Press,
Cambridge, MA, 2002.
[7] Bharath K. Sriperumbudur, Arthur Gretton, Kenji Fukumizu, Bernhard
Schölkopf and Gert R. G. Lanckriet, Hilbert space embeddings and
metrics on probability measures, Journal of Machine Learning Research
11 (2010), 1517-1561.
[8] Christopher M. Bishop, Pattern Recognition and Machine Learning,
Springer, 2006.
[9] C. Cortes and V. Vapnik, Support-vector networks, Machine Learning
20(3) (1995), 273-297.
DOI: https://doi.org/10.1007/BF00994018
[10] D. B. Thiyam, S. Cruces, J. Olias and A. Cichocki, Optimization
of alpha-beta log-det divergences and their application in the spatial
filtering of two class motor imagery movements, Entropy 19(3) (2017),
Article 89.
DOI: https://doi.org/10.3390/e19030089
[11] D. Olszewski and B. Ster, Asymmetric clustering using the
alpha-beta divergence, Pattern Recognition 47(5) (2014), 2031-2041.
DOI: https://doi.org/10.1016/j.patcog.2013.11.019
[12] F. Topsøe, Jenson-Shannon divergence and norm-based measures of
discrimination and variation, Preprint (2003).
[13] Trevor Hastie, Robert Tibshirani and Jerome Friedman, The
Elements of Statistical Learning, Springer Series in Statistics,
2001.
[14] Ho Chung Leon Law, Dougal J. Sutherland, Dino Sejdinovic and Seth
Flanman, Bayesian Approaches to Distribution Regression, Proceedings
of the 21st International Conference on Artificial Intelligence and
Statistics (AISTATS), Lanzarote, Spain, PMLR: Volume 84 (2018).
[15] I. J. Schoenberg, Metric space and positive definite functions,
Transactions of the American Mathematical Society 44(3) (1938),
522-536.
DOI: https://doi.org/10.1090/S0002-9947-1938-1501980-0
[16] F. Amara, M. Fezari and H. Bourouba, An improved GMM-SVM system
based on distance metric for voice pathology detection, Applied
Mathematics and Information Sciences An International Journal 10(3)
(2016), 1061-1070.
DOI: https://doi.org/10.18576/amis/100324
[17] I. W. Sumarjaya, A survey of kernel-type estimators for copula
and their applications, Journal of Physics: Conference Series 893
(2017), Article 012027.
DOI: https://doi.org/10.1088/1742-6596/893/1/012027
[18] J. Lafferty and G. Lebanon, Diffusion kernels on statistical
manifolds, Journal of Machine Learning Research 6 (2005), 129-163.
[19] C. van den Berg, J. P. R. Christensen and P. Ressel, Harmonic
Analysis on Semigroups, Springer, New York 29 (1984), 438.
[20] John Shawe-Taylor and Nello Cristianini, Kernel Methods for
Pattern Analysis, 2004.
[21] K. Muandet, K. Fukumizu, B. Sriperumbudur and B. Schölkopf,
Kernel mean embedding of distribution: A review and beyond,
Foundations and trends in Machine Learning 10(1-2) (2017), 1-141.
DOI: http://dx.doi.org/10.1561/2200000060
[22] M. Hein, T. N. Lal and O. Bousquet, Hilbertian Metrics on
Probability Measures and their Application in SMVs, Accepted at DAGM
Springer 3175 (2004), 270-277.
[23] M. Hein, O. Bousquet and B. Schölkopf, Maximal margin
classification for metric spaces, Journal of Computer and System
Sciences 71(3) (2005), 333-359.
DOI: https://doi.org/10.1016/j.jcss.2004.10.013
[24] M. Hein and O. Bousquet, Maximal margin classification for metric
spaces, In 16th Annual Conference on Learning Theory (COLT), 2003.
[25] O. Chapelle, P. Haffner and V. Vapnik, Support vector machines
for histogram-based image classification, IEEE Transaction on Neural
Networks 10(5) (1999), 1055-1064.
DOI: https://doi.org/10.1109/72.788646
[26] P. J. Moreno, P. P. Ho and N. Vasconcelos, A Kullback-Leibler
divergence based kernel for SVM classification in multimedia
application, NIPS 16 (2003).
[27] T. Jebara and R. Kondor, Bhattacharyya and Expected Likelihood
Kernels, In 16th Annual Conference on Learning Theory (COLT) (2003),
57-71.
[28] T. J. Abrahamsen, L. K. Hansen and O. Winther, Kernel Methods for
Machine Learning with Life Science Applications, Technical University
of Denmark (DTU), PHD, No. 299, 2013.
[29] V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.
