References
- P. Billingsley, Convergence of Probability Measures, Wiley, New York, 1968.
- M. Černý, J. Antoch, and M. Hladík, On the possibilistic approach to linear regression models involving uncertain, indeterminate or interval data, Inform. Sci. 244 (2013), pp. 26–47. doi: 10.1016/j.ins.2013.04.035
- X.R. Chu and H.W. Sun, Half supervised coefficient regularization for regression learning with unbounded sampling, Int. J. Comput. Math. 90(7) (2013), pp. 1321–1333. doi: 10.1080/00207160.2012.749985
- F. Cucker and D.X. Zhou, Learning Theory: An Approximation Theory Viewpoint, Cambridge University, Cambridge, 2007.
- G.E. Fasshauer, Toward approximate moving least squares approximation with irregularly spaced centers, Comput. Methods Appl. Mech. Engrg. 193 (2004), pp. 1231–1243. doi: 10.1016/j.cma.2003.12.017
- Z.C. Guo and L. Shi, Classification with non-i.i.d sampling, Math. Comput. Model. 54(5–6) (2011), pp. 1347–1364. doi: 10.1016/j.mcm.2011.03.042
- Q. Guo and P.X. Ye, Coefficient-based regularized regression with dependent and unbounded sampling, Int. J. Wavelets Multiresolut. Inf. Process. 14(5) (2016), pp. 14 pp. Article ID: 1650039, doi:10.1142/S0219691316500399.
- F.C. He, H. Chen, and L.Q. Li, Statistical analysis of the moving least-squares method with unbounded sampling, Inform. Sci. 268 (2014), pp. 370–380. doi: 10.1016/j.ins.2014.01.001
- Z. Komargodski and D. Levin, Hermite type moving-least-squares approximations, Comput. Math. Appl. 51(8) (2006), pp. 1223–1232. doi: 10.1016/j.camwa.2006.04.005
- X. Li, Error estimates for the moving least-square approximation and the element-free Galerkin method in n-dimensional spaces, Appl. Numer. Math. 99 (2016), pp. 77–97. doi: 10.1016/j.apnum.2015.07.006
- X.L. Li, H. Chen, and Y. Wang, Error analysis in Sobolev spaces for the improved moving least-square approximation and the improved element-free Galerkin method, Appl. Math. Comput. 262 (2015), pp. 56–78.
- X. Li and S. Li, Analysis of the complex moving least squares approximation and the associated element-free Galerkin method, Appl. Math. Model. 47 (2017), pp. 45–62. doi: 10.1016/j.apm.2017.03.019
- D.H. McLain, Drawing contours from arbitrary data points, Comput. J. 17(4) (1974), pp. 318–324. doi: 10.1093/comjnl/17.4.318
- D. Mirzaei, Analysis of moving least squares approximation revisited, J. Comput. Appl. Math. 282 (2015), pp. 237–250. doi: 10.1016/j.cam.2015.01.007
- Z.W. Pan and Q.W. Xiao, Least-square regularized regression with non-iid sampling, J. Statist. Plann. Inference 139(10) (2009), pp. 3579–3587. doi: 10.1016/j.jspi.2009.04.007
- A. Savitzky and M.J.E. Golay, Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem. 36(8) (1964), pp. 1627–1639. doi: 10.1021/ac60214a047
- D. Shepard, A two-dimensional interpolation function for irregularly-spaced data, Proc. of 23rd ACM National Conf., ACM, New York, 1968, pp. 517–524.
- S. Smale and D.X. Zhou, Online learning with Markov sampling, Anal. Appl. 7(1) (2009), pp. 87–113. doi: 10.1142/S0219530509001293
- I. Steinwart, D. Hush, and C. Scovel, Learning from dependent observations, J. Multivariate Anal. 100(1) (2009), pp. 175–194. doi: 10.1016/j.jmva.2008.04.001
- H.W. Sun and Q. Guo, Coefficient regularized regression with non-iid sampling, Int. J. Comput. Math. 88(15) (2011), pp. 3113–3124. doi: 10.1080/00207160.2011.587511
- M.P. Wand and M.C. Jones, Kernel Smoothing, Chapman and Hall, London, 1995.
- H.Y. Wang, Concentration estimates for the moving least-square method in learning theory, J. Approx. Theory 163(9) (2011), pp. 1125–1133. doi: 10.1016/j.jat.2011.03.006
- H.Y. Wang, D.H. Xiang, and D.X. Zhou, Moving least-square method in learning theory, J. Approx. Theory 162(3) (2010), pp. 599–614. doi: 10.1016/j.jat.2009.12.002
- Q. Wu, Y. Ying, and D.X. Zhou, Learning rates of least square regularized regression, Found. Comput. Math. 6(2) (2006), pp. 171–192. doi: 10.1007/s10208-004-0155-9