[1] Aronszajn, N.: Theory of reproducing kernels. Trans. Amer. Math. Soc. 68 (1950), 33–404. DOI 10.1090/S0002-9947-1950-0051437-7 | MR 0051437 | Zbl 0037.20701

[2] Berg, C., Christensen, J. P. R., Ressel, P.: Harmonic Analysis on Semigroups. Springer-Verlag, New York 1984. MR 0747302 | Zbl 0619.43001

[3] Bertero, M.: Linear inverse and ill-posed problems. Advances in Electronics and Electron Physics 75 (1989), 1–120.

[4] Bjorck, A.: Numerical methods for least squares problem. SIAM 1996. MR 1386889

[5] Cucker, F., Smale, S.: On the mathematical foundations of learning. Bull. Amer. Math. Soc. 39 (2001), 1–49. DOI 10.1090/S0273-0979-01-00923-5 | MR 1864085

[6] Friedman, A.: Modern Analysis. Dover, New York 1982. MR 0663003 | Zbl 0557.46001

[7] Girosi, F.: An equivalence between sparse approximation and support vector machines. Neural Computation 10 (1998), 1455–1480 (AI Memo No 1606, MIT). DOI 10.1162/089976698300017269

[8] Groetch, C. W.: Generalized Inverses of Linear Operators. Dekker, New York 1977.

[9] Kůrková, V.: High-dimensional approximation by neural networks. Chapter 4 in Advances in Learning Theory: Methods, Models and Applications (J. Stuykens et al., ed.) (2003), 69–88. IOS Press, Amsterdam.

[10] Kůrková, V.: Learning from data as an inverse problem. In Proc. of COMPSTAT 2004 (J. Antoch, ed.), Physica-Verlag, Heidelberg, 1377–1384. MR 2173152

[11] Kůrková, V., Sanguineti, M.: Error estimates for approximate optimization by the extended Ritz method. SIAM J. Optim. (to appear). MR 2144176

[12] Kůrková, V., Sanguineti, M.: Learning with generalization capability by kernel methods with bounded complexity. J. Compl. (to appear). MR 2138445

[13] Moore, E. H.: Abstract. Bulletin AMS 26 (1920), 394–395.

[14] Narcowich, F. J., Sivakumar, N., Ward, J. D.: On condition numbers associated with radial-function interpolation. J. Math. Anal. Appl. 186 (1994), 457–485. DOI 10.1006/jmaa.1994.1311 | MR 1293005 | Zbl 0813.65005

[15] Parzen, E.: An approach to time series analysis. Annals Math. Statistics 32 (1966), 951–989. DOI 10.1214/aoms/1177704840 | MR 0143315

[16] Penrose, R.: A generalized inverse for matrices. Proc. Cambridge Philos. Soc. 52 (1955), 406–413. MR 0069793 | Zbl 0065.24603

[17] Poggio, T., Girosi, F.: Networks for approximation and learning. Proc. IEEE 78 (1990), 1481–1497.

[18] Poggio, T., Smale, S.: The mathematics of learning: dealing with data. Notices Amer. Math. Soc. 50 (2003), 536–544. MR 1968413 | Zbl 1083.68100

[19] Sejnowski, T. J., Rosenberg, C.: Parallel networks that learn to pronounce English text. Complex Systems 1 (1987), 145–168. Zbl 0655.68107

[20] Tichonov, A. N., Arsenin, V. Y.: Solutions of Ill-posed Problems. W. H. Winston, Washington, D. C. 1977.

[21] Wahba, G.: Splines Models for Observational Data. SIAM, Philadelphia 1990. MR 1045442

[22] Werbos, P. J.: Backpropagation: Basics and New Developments. The Handbook of Brain Theory and Neural Networks (M. Arbib, ed.), 134–139. MIT Press, Cambridge 1995.