Kolmogorov's Theorem Is Relevant. - Vera Kurková (Neural Comput., 1991)

Universal Approximation Using Feedforward Neural Networks with Gaussian Bar Units. - Vera Kurková (ECAI, 1992)

Kolmogorov's theorem and multilayer neural networks. - Vera Kurková (Neural Networks, 1992)

Approximation of continuous functions by RBF and KBF networks. - Vera Kurková; Katerina Hlavácková (ESANN, 1994)

Functionally Equivalent Feedforward Neural Networks. - Vera Kurková; Paul C. Kainen (Neural Comput., 1994)

Uniqueness of network parametrization and faster learning. - Paul C. Kainen; Vera Kurková; Vladik Kreinovich; Ongard Sirisaengtaksin (Neural Parallel Sci. Comput., 1994)

Approximation of functions by Gaussian RBF networks with bouded number of hidden units. - Vera Kurková (ESANN, 1995)

Approximation of functions by perceptron networks with bounded number of hidden units. - Vera Kurková (Neural Networks, 1995)

Rates of approximation of real-valued boolean functions by neural networks. - Katerina Hlavácková; Vera Kurková (ESANN, 1996)

A geometric method to obtain error-correcting classification by neural networks with fewer hidden units. - Vera Kurková; Paul C. Kainen (ICNN, 1996)

Upper Bounds on the Approximation Rates of Real-valued Boolean Functions by Neural Networks. - Katerina Hlavácková; Vera Kurková; Petr Savický (ICANNGA, 1997)

Estimates of the Number of Hidden Units and Variation with Respect to Half-Spaces. - Vera Kurková; Paul C. Kainen; Vladik Kreinovich (Neural Networks, 1997)

Approximation of Functions by Neural Networks. - Vera Kurková (NC, 1998)

Representations and rates of approximation of real-valued Boolean functions by neural networks. - Vera Kurková; Petr Savický; Katerina Hlavácková (Neural Networks, 1998)

Approximation by neural networks is not continuous. - Paul C. Kainen; Vera Kurková; Andrew Vogt (Neurocomputing, 1999)

Comparison of Rates of Linear and Neural Network Approximation. - Vera Kurková; Marcello Sanguineti (IJCNN (1), 2000)

Best approximation by Heaviside perceptron networks. - Paul C. Kainen; Vera Kurková; Andrew Vogt (Neural Networks, 2000)

Tight Bounds on Rates of Neural-Network Approximation. - Vera Kurková; Marcello Sanguineti (ICANN, 2001)

Continuity of Approximation by Neural Networks in Lp Spaces. - Paul C. Kainen; Vera Kurková; Andrew Vogt (Ann. Oper. Res., 2001)

Bounds on rates of variable-basis and neural-network approximation. - Vera Kurková; Marcello Sanguineti (IEEE Trans. Inf. Theory, 2001)

Comparison of worst case errors in linear and neural network approximation. - Vera Kurková; Marcello Sanguineti (IEEE Trans. Inf. Theory, 2002)

Neural network learning as approximate optimization. - Vera Kurková; Marcello Sanguineti (ICANNGA, 2003)

Best approximation by linear combinations of characteristic functions of half-spaces. - Paul C. Kainen; Vera Kurková; Andrew Vogt (J. Approx. Theory, 2003)

Minimization of Error Functionals over Variable-Basis Functions. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (SIAM J. Optim., 2004)

Neural Network Learning as an Inverse Problem. - Vera Kurková (Log. J. IGPL, 2005)

Learning with generalization capability by kernel methods of bounded complexity. - Vera Kurková; Marcello Sanguineti (J. Complex., 2005)

Rates of Minimization of Error Functionals over Boolean Variable-Basis Functions. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (J. Math. Model. Algorithms, 2005)

Error Estimates for Approximate Optimization by the Extended Ritz Method. - Vera Kurková; Marcello Sanguineti (SIAM J. Optim., 2005)

Estimates of Approximation Rates by Gaussian Radial-Basis Functions. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (ICANNGA (2), 2007)

Estimates of Data Complexity in Neural-Network Learning. - Vera Kurková (SOFSEM (1), 2007)

Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets. - Vera Kurková; Marcello Sanguineti (Discret. Appl. Math., 2007)

A Sobolev-type upper bound for rates of approximation by linear combinations of Heaviside plane waves. - Paul C. Kainen; Vera Kurková; Andrew Vogt (J. Approx. Theory, 2007)

Generalization in Learning from Examples. - Vera Kurková (Challenges for Computational Intelligence, 2007)

Estimates of Network Complexity and Integral Representations. - Paul C. Kainen; Vera Kurková (ICANN (1), 2008)

Geometric Rates of Approximation by Neural Networks. - Vera Kurková; Marcello Sanguineti (SOFSEM, 2008)

Approximate Minimization of the Regularized Expected Error over Kernel Models. - Vera Kurková; Marcello Sanguineti (Math. Oper. Res., 2008)

Minimization of Error Functionals over Perceptron Networks. - Vera Kurková (Neural Comput., 2008)

Geometric Upper Bounds on Rates of Variable-Basis Approximation. - Vera Kurková; Marcello Sanguineti (IEEE Trans. Inf. Theory, 2008)

Model Complexity of Neural Networks and Integral Transforms. - Vera Kurková (ICANN (1), 2009)

On Tractability of Neural-Network Approximation. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (ICANNGA, 2009)

Complexity of Gaussian-radial-basis networks approximating smooth functions. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (J. Complex., 2009)

An Integral Upper Bound for Neural Network Approximation. - Paul C. Kainen; Vera Kurková (Neural Comput., 2009)

Estimates of Model Complexity in Neural-Network Learning. - Vera Kurková (Innovations in Neural Information Paradigms and Applications, 2009)

Some Comparisons of Model Complexity in Linear and Neural-Network Approximation. - Giorgio Gnecco; Vera Kurková; Marcello Sanguineti (ICANN (3), 2010)

Inverse Problems in Learning from Data. - Vera Kurková (IJCCI (ICFC-ICNC), 2010)

Learning from Data as an Optimization and Inverse Problem. - Vera Kurková (IJCCI (Selected Papers), 2010)

Editorial. - Vera Kurková; Roman Neruda; Jan Koutník (Neural Networks, 2010)

Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks. - Giorgio Gnecco; Vera Kurková; Marcello Sanguineti (ICANN (1), 2011)

Kernel Networks with Fixed and Variable Widths. - Vera Kurková; Paul C. Kainen (ICANNGA (1), 2011)

Some comparisons of complexity in dictionary-based and linear computational models. - Giorgio Gnecco; Vera Kurková; Marcello Sanguineti (Neural Networks, 2011)

Can dictionary-based computational models outperform the best linear ones? - Giorgio Gnecco; Vera Kurková; Marcello Sanguineti (Neural Networks, 2011)

Some Comparisons of Networks with Radial and Kernel Units. - Vera Kurková (ICANN (2), 2012)

Surrogate Modelling of Solutions of Integral Equations by Neural Networks. - Vera Kurková (AIAI (1), 2012)

Surrogate solutions of Fredholm equations by feedforward networks. - Vera Kurková (ITAT, 2012)

Accuracy of approximations of solutions to Fredholm equations by kernel methods. - Giorgio Gnecco; Vera Kurková; Marcello Sanguineti (Appl. Math. Comput., 2012)

Guest editorial: Adaptive and natural computing algorithms. - Vera Kurková (Neurocomputing, 2012)

Complexity estimates based on integral transforms induced by computational units. - Vera Kurková (Neural Networks, 2012)

Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (IEEE Trans. Inf. Theory, 2012)

Model Complexity of Neural Networks in High-Dimensional Approximation. - Vera Kurková (Recent Advances in Intelligent Engineering Systems, 2012)

Can Two Hidden Layers Make a Difference? - Vera Kurková; Marcello Sanguineti (ICANNGA, 2013)

Approximating Multivariable Functions by Feedforward Neural Nets. - Paul C. Kainen; Vera Kurková; Marcello Sanguineti (Handbook on Neural Information Processing, 2013)

Representations of Highly-Varying Functions by One-Hidden-Layer Networks. - Vera Kurková (ICAISC (1), 2014)

Complexity of Shallow Networks Representing Functions with Large Variations. - Vera Kurková; Marcello Sanguineti (ICANN, 2014)

Comparing fixed and variable-width Gaussian networks. - Vera Kurková; Paul C. Kainen (Neural Networks, 2014)

Complexity of Shallow Networks Representing Finite Mappings. - Vera Kurková (ICAISC (1), 2015)

Limitations of One-Hidden-Layer Perceptron Networks. - Vera Kurková (ITAT, 2015)

Lower Bounds on Complexity of Shallow Perceptron Networks. - Vera Kurková (EANN, 2016)

Multivariable Approximation by Convolutional Kernel Networks. - Vera Kurková (ITAT, 2016)

Model complexities of shallow networks representing highly varying functions. - Vera Kurková; Marcello Sanguineti (Neurocomputing, 2016)

Sparsity of Shallow Networks Representing Finite Mappings. - Vera Kurková (EANN, 2017)

Bounds on Sparsity of One-Hidden-Layer Perceptron Networks. - Vera Kurková (ITAT, 2017)

Probabilistic lower bounds for approximation by shallow perceptron networks. - Vera Kurková; Marcello Sanguineti (Neural Networks, 2017)

Sparsity and Complexity of Networks Computing Highly-Varying Functions. - Vera Kurková (ICANN (3), 2018)

Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks. - Vera Kurková; Marcello Sanguineti (ITAT, 2018)

Constructive lower bounds on model complexity of shallow perceptron networks. - Vera Kurková (Neural Comput. Appl., 2018)

Probabilistic Bounds for Approximation by Neural Networks. - Vera Kurková (ICANN (1), 2019)

Limitations of Shallow Networks. - Vera Kurková (INNSBDDL (Tutorials), 2019)

Probabilistic Bounds for Binary Classification of Large Data Sets. - Vera Kurková; Marcello Sanguineti (INNSBDDL, 2019)

Limitations of shallow networks representing finite mappings. - Vera Kurková (Neural Comput. Appl., 2019)

Classification by Sparse Neural Networks. - Vera Kurková; Marcello Sanguineti (IEEE Trans. Neural Networks Learn. Syst., 2019)

Approximative compactness of linear combinations of characteristic functions. - Paul C. Kainen; Vera Kurková; Andrew Vogt (J. Approx. Theory, 2020)

Brain-inspired computing and machine learning. - Lazaros S. Iliadis; Vera Kurková; Barbara Hammer (Neural Comput. Appl., 2020)

Correlations of random classifiers on large data sets. - Vera Kurková; Marcello Sanguineti (Soft Comput., 2021)

Translation-Invariant Kernels for Multivariable Approximation. - Vera Kurková; David Coufal (IEEE Trans. Neural Networks Learn. Syst., 2021)

Approximation of Binary-Valued Functions by Networks of Finite VC Dimension. - Vera Kurková (ICANN (1), 2023)

Approximation of classifiers by deep perceptron networks. - Vera Kurková; Marcello Sanguineti (Neural Networks, 2023)

Some Comparisons of Linear and Deep ReLU Network Approximation. - Vera Kurková (ICANN (10), 2024)

Networks with Finite VC Dimension: Pro and Contra. - Vera Kurková; Marcello Sanguineti (CoRR, 2025)