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2020 – today
- 2024
[c40]Vera Kurková
:
Some Comparisons of Linear and Deep ReLU Network Approximation. ICANN (10) 2024: 231-240- 2023
- [j43]Vera Kurková, Marcello Sanguineti:
Approximation of classifiers by deep perceptron networks. Neural Networks 165: 654-661 (2023) - [c39]Vera Kurková:
Approximation of Binary-Valued Functions by Networks of Finite VC Dimension. ICANN (1) 2023: 483-490 - 2021
- [j42]Vera Kurková, Marcello Sanguineti:
Correlations of random classifiers on large data sets. Soft Comput. 25(19): 12641-12648 (2021) - [j41]Vera Kurková, David Coufal:
Translation-Invariant Kernels for Multivariable Approximation. IEEE Trans. Neural Networks Learn. Syst. 32(11): 5072-5081 (2021) - 2020
- [j40]Paul C. Kainen, Vera Kurková, Andrew Vogt:
Approximative compactness of linear combinations of characteristic functions. J. Approx. Theory 257: 105435 (2020) - [j39]Lazaros S. Iliadis, Vera Kurková, Barbara Hammer:
Brain-inspired computing and machine learning. Neural Comput. Appl. 32(11): 6641-6643 (2020)
2010 – 2019
- 2019
- [j38]Vera Kurková:
Limitations of shallow networks representing finite mappings. Neural Comput. Appl. 31(6): 1783-1792 (2019) - [j37]Vera Kurková, Marcello Sanguineti:
Classification by Sparse Neural Networks. IEEE Trans. Neural Networks Learn. Syst. 30(9): 2746-2754 (2019) - [c38]Vera Kurková:
Probabilistic Bounds for Approximation by Neural Networks. ICANN (1) 2019: 418-428 - [c37]Vera Kurková:
Limitations of Shallow Networks. INNSBDDL (Tutorials) 2019: 129-154 - [c36]Vera Kurková, Marcello Sanguineti:
Probabilistic Bounds for Binary Classification of Large Data Sets. INNSBDDL 2019: 309-319 - [e12]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Theoretical Neural Computation - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11727, Springer 2019, ISBN 978-3-030-30486-7 [contents] - [e11]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Deep Learning - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11728, Springer 2019, ISBN 978-3-030-30483-6 [contents] - [e10]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Image Processing - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part III. Lecture Notes in Computer Science 11729, Springer 2019, ISBN 978-3-030-30507-9 [contents] - [e9]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019: Text and Time Series - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part IV. Lecture Notes in Computer Science 11730, Springer 2019, ISBN 978-3-030-30489-8 [contents] - [e8]Igor V. Tetko, Vera Kurková, Pavel Karpov, Fabian J. Theis:
Artificial Neural Networks and Machine Learning - ICANN 2019 - 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings - Workshop and Special Sessions. Lecture Notes in Computer Science 11731, Springer 2019, ISBN 978-3-030-30492-8 [contents] - 2018
- [j36]Vera Kurková:
Constructive lower bounds on model complexity of shallow perceptron networks. Neural Comput. Appl. 29(7): 305-315 (2018) - [c35]Vera Kurková:
Sparsity and Complexity of Networks Computing Highly-Varying Functions. ICANN (3) 2018: 534-543 - [c34]Vera Kurková, Marcello Sanguineti:
Probabilistic Bounds on Complexity of Networks Computing Binary Classification Tasks. ITAT 2018: 86-91 - [e7]Vera Kurková, Yannis Manolopoulos, Barbara Hammer, Lazaros S. Iliadis, Ilias Maglogiannis:
Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I. Lecture Notes in Computer Science 11139, Springer 2018, ISBN 978-3-030-01417-9 [contents] - [e6]Vera Kurková, Yannis Manolopoulos, Barbara Hammer, Lazaros S. Iliadis, Ilias Maglogiannis:
Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II. Lecture Notes in Computer Science 11140, Springer 2018, ISBN 978-3-030-01420-9 [contents] - [e5]Vera Kurková, Yannis Manolopoulos, Barbara Hammer, Lazaros S. Iliadis, Ilias Maglogiannis:
Artificial Neural Networks and Machine Learning - ICANN 2018 - 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III. Lecture Notes in Computer Science 11141, Springer 2018, ISBN 978-3-030-01423-0 [contents] - 2017
- [j35]Vera Kurková, Marcello Sanguineti:
Probabilistic lower bounds for approximation by shallow perceptron networks. Neural Networks 91: 34-41 (2017) - [c33]Vera Kurková:
Sparsity of Shallow Networks Representing Finite Mappings. EANN 2017: 337-348 - [c32]Vera Kurková:
Bounds on Sparsity of One-Hidden-Layer Perceptron Networks. ITAT 2017: 100-105 - 2016
- [j34]Vera Kurková, Marcello Sanguineti:
Model complexities of shallow networks representing highly varying functions. Neurocomputing 171: 598-604 (2016) - [c31]Vera Kurková:
Lower Bounds on Complexity of Shallow Perceptron Networks. EANN 2016: 283-294 - [c30]Vera Kurková:
Multivariable Approximation by Convolutional Kernel Networks. ITAT 2016: 118-122 - 2015
- [c29]Vera Kurková:
Complexity of Shallow Networks Representing Finite Mappings. ICAISC (1) 2015: 39-48 - [c28]Vera Kurková:
Limitations of One-Hidden-Layer Perceptron Networks. ITAT 2015: 167-171 - 2014
- [j33]Vera Kurková, Paul C. Kainen:
Comparing fixed and variable-width Gaussian networks. Neural Networks 57: 23-28 (2014) - [c27]Vera Kurková:
Representations of Highly-Varying Functions by One-Hidden-Layer Networks. ICAISC (1) 2014: 67-76 - [c26]Vera Kurková, Marcello Sanguineti:
Complexity of Shallow Networks Representing Functions with Large Variations. ICANN 2014: 331-338 - [e4]Vera Kurková, Lukás Bajer, Vojtech Svátek:
Proceedings of the main track of the 14th Conference on Information Technologies - Applications and Theory (ITAT 2014), with selected papers from Znalosti 2014 collocated with Znalosti 2014, Demanovska Dolina - Jasna, Slovakia, September 25 - 29, 2014. CEUR Workshop Proceedings 1214, CEUR-WS.org 2014 [contents] - 2013
- [c25]Vera Kurková, Marcello Sanguineti:
Can Two Hidden Layers Make a Difference? ICANNGA 2013: 30-39 - [p4]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
Approximating Multivariable Functions by Feedforward Neural Nets. Handbook on Neural Information Processing 2013: 143-181 - 2012
- [j32]Giorgio Gnecco, Vera Kurková, Marcello Sanguineti:
Accuracy of approximations of solutions to Fredholm equations by kernel methods. Appl. Math. Comput. 218(14): 7481-7497 (2012) - [j31]Vera Kurková:
Guest editorial: Adaptive and natural computing algorithms. Neurocomputing 96: 1 (2012) - [j30]Vera Kurková:
Complexity estimates based on integral transforms induced by computational units. Neural Networks 33: 160-167 (2012) - [j29]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
Dependence of Computational Models on Input Dimension: Tractability of Approximation and Optimization Tasks. IEEE Trans. Inf. Theory 58(2): 1203-1214 (2012) - [c24]Vera Kurková:
Some Comparisons of Networks with Radial and Kernel Units. ICANN (2) 2012: 17-24 - [c23]Vera Kurková:
Surrogate Modelling of Solutions of Integral Equations by Neural Networks. AIAI (1) 2012: 88-96 - [c22]Vera Kurková:
Surrogate solutions of Fredholm equations by feedforward networks. ITAT 2012: 49-54 - [p3]Vera Kurková:
Model Complexity of Neural Networks in High-Dimensional Approximation. Recent Advances in Intelligent Engineering Systems 2012: 151-160 - 2011
- [j28]Giorgio Gnecco, Vera Kurková, Marcello Sanguineti:
Some comparisons of complexity in dictionary-based and linear computational models. Neural Networks 24(2): 171-182 (2011) - [j27]Giorgio Gnecco, Vera Kurková, Marcello Sanguineti:
Can dictionary-based computational models outperform the best linear ones? Neural Networks 24(8): 881-887 (2011) - [c21]Giorgio Gnecco, Vera Kurková, Marcello Sanguineti:
Bounds for Approximate Solutions of Fredholm Integral Equations Using Kernel Networks. ICANN (1) 2011: 126-133 - [c20]Vera Kurková, Paul C. Kainen:
Kernel Networks with Fixed and Variable Widths. ICANNGA (1) 2011: 12-21 - 2010
- [j26]Vera Kurková, Roman Neruda, Jan Koutník:
Editorial. Neural Networks 23(4): 465 (2010) - [c19]Giorgio Gnecco, Vera Kurková, Marcello Sanguineti:
Some Comparisons of Model Complexity in Linear and Neural-Network Approximation. ICANN (3) 2010: 358-367 - [c18]Vera Kurková:
Inverse Problems in Learning from Data. IJCCI (ICFC-ICNC) 2010: 316-321 - [c17]Vera Kurková:
Learning from Data as an Optimization and Inverse Problem. IJCCI (Selected Papers) 2010: 361-372
2000 – 2009
- 2009
- [j25]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
Complexity of Gaussian-radial-basis networks approximating smooth functions. J. Complex. 25(1): 63-74 (2009) - [j24]Paul C. Kainen, Vera Kurková:
An Integral Upper Bound for Neural Network Approximation. Neural Comput. 21(10): 2970-2989 (2009) - [c16]Vera Kurková:
Model Complexity of Neural Networks and Integral Transforms. ICANN (1) 2009: 708-717 - [c15]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
On Tractability of Neural-Network Approximation. ICANNGA 2009: 11-21 - [p2]Vera Kurková:
Estimates of Model Complexity in Neural-Network Learning. Innovations in Neural Information Paradigms and Applications 2009: 97-111 - 2008
- [j23]Vera Kurková, Marcello Sanguineti:
Approximate Minimization of the Regularized Expected Error over Kernel Models. Math. Oper. Res. 33(3): 747-756 (2008) - [j22]Vera Kurková:
Minimization of Error Functionals over Perceptron Networks. Neural Comput. 20(1): 252-270 (2008) - [j21]Vera Kurková, Marcello Sanguineti:
Geometric Upper Bounds on Rates of Variable-Basis Approximation. IEEE Trans. Inf. Theory 54(12): 5681-5688 (2008) - [c14]Paul C. Kainen, Vera Kurková:
Estimates of Network Complexity and Integral Representations. ICANN (1) 2008: 31-40 - [c13]Vera Kurková, Marcello Sanguineti:
Geometric Rates of Approximation by Neural Networks. SOFSEM 2008: 541-550 - [e3]Vera Kurková, Roman Neruda, Jan Koutník:
Artificial Neural Networks - ICANN 2008 , 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part I. Lecture Notes in Computer Science 5163, Springer 2008, ISBN 978-3-540-87535-2 [contents] - [e2]Vera Kurková, Roman Neruda, Jan Koutník:
Artificial Neural Networks - ICANN 2008, 18th International Conference, Prague, Czech Republic, September 3-6, 2008, Proceedings, Part II. Lecture Notes in Computer Science 5164, Springer 2008, ISBN 978-3-540-87558-1 [contents] - 2007
- [j20]Vera Kurková, Marcello Sanguineti:
Estimates of covering numbers of convex sets with slowly decaying orthogonal subsets. Discret. Appl. Math. 155(15): 1930-1942 (2007) - [j19]Paul C. Kainen, Vera Kurková, Andrew Vogt:
A Sobolev-type upper bound for rates of approximation by linear combinations of Heaviside plane waves. J. Approx. Theory 147(1): 1-10 (2007) - [c12]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
Estimates of Approximation Rates by Gaussian Radial-Basis Functions. ICANNGA (2) 2007: 11-18 - [c11]Vera Kurková:
Estimates of Data Complexity in Neural-Network Learning. SOFSEM (1) 2007: 377-387 - [p1]Vera Kurková:
Generalization in Learning from Examples. Challenges for Computational Intelligence 2007: 343-363 - 2005
- [j18]Vera Kurková:
Neural Network Learning as an Inverse Problem. Log. J. IGPL 13(5): 551-559 (2005) - [j17]Vera Kurková, Marcello Sanguineti:
Learning with generalization capability by kernel methods of bounded complexity. J. Complex. 21(3): 350-367 (2005) - [j16]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
Rates of Minimization of Error Functionals over Boolean Variable-Basis Functions. J. Math. Model. Algorithms 4(4): 355-368 (2005) - [j15]Vera Kurková, Marcello Sanguineti:
Error Estimates for Approximate Optimization by the Extended Ritz Method. SIAM J. Optim. 15(2): 461-487 (2005) - 2004
- [j14]Paul C. Kainen, Vera Kurková, Marcello Sanguineti:
Minimization of Error Functionals over Variable-Basis Functions. SIAM J. Optim. 14(3): 732-742 (2004) - 2003
- [j13]Paul C. Kainen, Vera Kurková, Andrew Vogt:
Best approximation by linear combinations of characteristic functions of half-spaces. J. Approx. Theory 122(2): 151-159 (2003) - [c10]Vera Kurková, Marcello Sanguineti:
Neural network learning as approximate optimization. ICANNGA 2003: 53-57 - 2002
- [j12]Vera Kurková, Marcello Sanguineti:
Comparison of worst case errors in linear and neural network approximation. IEEE Trans. Inf. Theory 48(1): 264-275 (2002) - 2001
- [j11]Paul C. Kainen, Vera Kurková, Andrew Vogt:
Continuity of Approximation by Neural Networks in Lp Spaces. Ann. Oper. Res. 101(1-4): 143-147 (2001) - [j10]Vera Kurková, Marcello Sanguineti:
Bounds on rates of variable-basis and neural-network approximation. IEEE Trans. Inf. Theory 47(6): 2659-2665 (2001) - [c9]Vera Kurková, Marcello Sanguineti:
Tight Bounds on Rates of Neural-Network Approximation. ICANN 2001: 277-284 - 2000
- [j9]Paul C. Kainen, Vera Kurková, Andrew Vogt:
Best approximation by Heaviside perceptron networks. Neural Networks 13(7): 695-697 (2000) - [c8]Vera Kurková, Marcello Sanguineti:
Comparison of Rates of Linear and Neural Network Approximation. IJCNN (1) 2000: 277-282
1990 – 1999
- 1999
- [j8]Paul C. Kainen, Vera Kurková, Andrew Vogt:
Approximation by neural networks is not continuous. Neurocomputing 29(1-3): 47-56 (1999) - 1998
- [j7]Vera Kurková, Petr Savický, Katerina Hlavácková:
Representations and rates of approximation of real-valued Boolean functions by neural networks. Neural Networks 11(4): 651-659 (1998) - [c7]Vera Kurková:
Approximation of Functions by Neural Networks. NC 1998: 29-35 - [e1]Mirek Kárný, Kevin Warwick, Vera Kurková:
Dealing with Complexity. Perspectives in Neural Computing, Springer 1998, ISBN 978-3-540-76160-0 - 1997
- [j6]Vera Kurková, Paul C. Kainen, Vladik Kreinovich:
Estimates of the Number of Hidden Units and Variation with Respect to Half-Spaces. Neural Networks 10(6): 1061-1068 (1997) - [c6]Katerina Hlavácková, Vera Kurková, Petr Savický:
Upper Bounds on the Approximation Rates of Real-valued Boolean Functions by Neural Networks. ICANNGA 1997: 495-499 - 1996
- [c5]Katerina Hlavácková, Vera Kurková:
Rates of approximation of real-valued boolean functions by neural networks. ESANN 1996 - [c4]Vera Kurková, Paul C. Kainen:
A geometric method to obtain error-correcting classification by neural networks with fewer hidden units. ICNN 1996: 1227-1232 - 1995
- [j5]Vera Kurková:
Approximation of functions by perceptron networks with bounded number of hidden units. Neural Networks 8(5): 745-750 (1995) - [c3]Vera Kurková:
Approximation of functions by Gaussian RBF networks with bouded number of hidden units. ESANN 1995 - 1994
- [j4]Vera Kurková, Paul C. Kainen:
Functionally Equivalent Feedforward Neural Networks. Neural Comput. 6(3): 543-558 (1994) - [j3]Paul C. Kainen, Vera Kurková, Vladik Kreinovich, Ongard Sirisaengtaksin:
Uniqueness of network parametrization and faster learning. Neural Parallel Sci. Comput. 2(4): 459-466 (1994) - [c2]Vera Kurková, Katerina Hlavácková:
Approximation of continuous functions by RBF and KBF networks. ESANN 1994 - 1992
- [j2]Vera Kurková:
Kolmogorov's theorem and multilayer neural networks. Neural Networks 5(3): 501-506 (1992) - [c1]Vera Kurková:
Universal Approximation Using Feedforward Neural Networks with Gaussian Bar Units. ECAI 1992: 193-197 - 1991
- [j1]Vera Kurková:
Kolmogorov's Theorem Is Relevant. Neural Comput. 3(4): 617-622 (1991)
Coauthor Index
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last updated on 2024-10-23 21:25 CEST by the dblp team
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