This is an interesting question and I did a quick research.
The OP asked about regression for continuous data. But the paper cited by @Vikram only works for classification.
Lu, Z., Kaye, J., & Leen, T. K. (2009). Hierarchical Fisher Kernels
for Longitudinal Data. In Advances in Neural Information Processing
A related paper for regression I found is the following. Technical details can be found in Section 2.3.
Seok, K. H., Shim, J., Cho, D., Noh, G. J., & Hwang, C. (2011).
Semiparametric mixed-effect least squares support vector machine for
analyzing pharmacokinetic and pharmacodynamic data. Neurocomputing,
No public software is found but the authors claimed the ease of use at the end of the paper.
The main advantage of the proposed LS-SVM ... is that regression estimators
can be easily computed by softwares solving a simple linear
equation system. This makes it easier to apply the proposed
approach to the analysis of repeated measurement data in practice.
To elaborate a bit more, there are two approaches for regression analysis using SVM (support vector machine):
- support vector regression (SVR) [Drucker, Harris; Burges, Christopher J. C.; Kaufman, Linda; Smola, Alexander J.; and Vapnik, Vladimir N. (1997); "Support Vector Regression Machines", in Advances in Neural Information Processing Systems 9, NIPS 1996, 155–161]
- least squares support vector machine (LS-SVM) [Suykens, Johan A. K.; Vandewalle, Joos P. L.; Least squares support vector machine classifiers, Neural Processing Letters, vol. 9, no. 3, Jun. 1999, pp. 293–300.]
The aforementioned Seol et al. (2011) adopted the LS-VSM approach.