Vĕra Kůrková - chapters and articles in books

• V. Kůrková:Limitations of shallow networks., In Recent Trends in Learning from Data, SCI 896, (Eds. L. Oneto, N. Navarin, A. Sperduti, and D. Anguita), (pp. 129 -154). Springer, 2020.
• P. C. Kainen, V. Kůrková: Quasiorthogonal dimension., In Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy, etc. Methods and Their Applications, (Eds. O. Kosheleva, S. Shary, G. Xiang, and R. Zapatrin), Springer, 2018.
• V. Kůrková, P. C. Kainen: Integral transforms induced by Heaviside perceptrons., In Beyond Traditional Probabilistic Data Processing Techniques: Interval, Fuzzy, etc. Methods and Their Applications, (Eds. O. Kosheleva, S. Shary, G. Xiang, and R. Zapatrin), Springer, 2018.
• V. Kůrková: Accuracy of Surrogate Solutions of Integral Equations by Feedforward Networks, Issues and Challenges of Intelligent Systems and Computational Intelligence (Eds. L. T. Kóczy et al.), Studies in Computational Intelligence 530, Springer International Publishing Switzerland, pp. 91-102, 2014.
• Paul C. Kainen, V. Kůrková, M. Sanguineti: Approximating Multivariable Functions by Feedforward Neural Nets (Chapter 5), Handbook on Neural Information Processing (Eds. M. Bianchini et al.), ISRL 49, Springer-Verlag Berlin Heidelberg, pp. 143-181, 2013.
• V. Kůrková: Estimates of Model Complexity in Neural-Network Learning. In Innovations in Neural Infor. Paradigms & Appli., (Eds. M. Bianchini et al.) (pp. 97-111). SCI 247, 2009.
• V. Kůrková: Generalization in learning from examples. In Challenges for Computational Intelligence SC 63 (Eds. W. Duch. J. Mandziuk), pp. 343-363. Berlin, Heidelberg: Springer-Verlag, 2007.
• V. Kůrková: Učení neuronových sítí se schopností generalizace. In Mysel', inteligencia a život (Eds. V. Kvasnička, P. Trebatický, J. Pospíchal, J. Kelemen) (pp. 275-286). Bratislava: STU, 2007.
• V. Kůrková: Mappings between high-dimensional representations in connectionistic systems. In Machine Intelligence: Quo Vadis? (Eds. P. Sinčák, J. Vasčák, K. Hirota) (pp. 31-45). Singapore: World Scientific, 2004.
• V. Kůrková: Aproximace funkcí neuronovými sítěmi. Chapter 8 in Umělá inteligence IV (Eds. V. Mařík, O. Štěpánková, J. Lažanský) (pp. 254-275). Praha: Academia, 2003.
• V. Kůrková: High-dimensional approximation and optimization by neural networks. Chapter 4 in Advances in Learning Theory: Methods, Models and Applications. (Eds. J. Suykens et al.) (pp. 69-88). Amsterdam: IOS Press, 2003.
• V. Kůrková: Neural networks as universal approximators. In The Handbook of Brain Theory and Neural Networks II (Ed. M. Arbib) (pp. 1180-1183). Cambridge: MIT Press, 2002.
• V. Kůrková: Universality and complexity of approximation of multivariable functions by feedforward networks. In Softcomputing and Industry: Recent Applications (Eds. R. Roy, M. Koeppen, S. Ovaska, T. Furuhashi, F. Hoffmann) (pp. 13-24). London: Springer-Verlag, 2002.
• V. Kůrková: Rates of approximation by neural networks. In Quo Vadis Computational Intelligence? (Eds. P. Sinčák, J. Vasčák (pp. 23-36). Berlin: Springer, 2000.
• V. Kůrková: Incremental approximation by neural networks. Chapter 12 in Complexity: Neural Network Approach. (Eds. K. Warwick, M. Kárný, V. Kůrková). (pp. 177-188). London: Springer-Verlag, 1998.
• V. Kůrková: Kolmogorov's theorem. In The Handbook of Brain Theory and Neural Networks (Ed. M. Arbib) (pp. 501-502). Cambridge: MIT Press, 1995.