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Leszek Plaskota

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2007
23EEBoleslaw Z. Kacewicz, Leszek Plaskota, Grzegorz W. Wasilkowski: Issue dedicated to Professor Henryk Wozniakowski. J. Complexity 23(4-6): 421-422 (2007)
2006
22EELeszek Plaskota, Ian H. Sloan: Guest Editors' preface. J. Complexity 22(5): (2006)
2005
21 Mark A. Kon, Leszek Plaskota, Andrzej W. Przybyszewski: Statistical Likelihood Representations of Prior Knowledge in Machine Learning. Artificial Intelligence and Applications 2005: 467-472
20 Mark A. Kon, Leszek Plaskota, Andrzej W. Przybyszewski: Machine Learning and Statistical MAP Methods. Intelligent Information Systems 2005: 441-445
19EEMark A. Kon, Leszek Plaskota: Information-based nonlinear approximation: an average case setting. J. Complexity 21(2): 211-229 (2005)
2004
18EELeszek Plaskota: Information-Based Nonlinear Approximation: An Average Case Setting. Algorithms and Complexity for Continuous Problems 2004
17EELeszek Plaskota, Klaus Ritter, Grzegorz W. Wasilkowski: Optimal designs for weighted approximation and integration of stochastic processes on [0, infinity). J. Complexity 20(1): 108-131 (2004)
16EELeszek Plaskota, Klaus Ritter: Guest Editors' Preface. J. Complexity 20(5): 592 (2004)
15 Piotr Gajda, Youming Li, Leszek Plaskota, Grzegorz W. Wasilkowski: A Monte Carlo algorithm for weighted integration over Reald. Math. Comput. 73(246): 813-825 (2004)
2002
14EELeszek Plaskota, Klaus Ritter, Grzegorz W. Wasilkowski: Average Case Complexity of Weighted Approximation and Integration over R+. J. Complexity 18(2): 517-544 (2002)
2001
13EEMark A. Kon, Leszek Plaskota: Complexity of Neural Network Approximation with Limited Information: A Worst Case Approach. J. Complexity 17(2): 345-365 (2001)
12EELeszek Plaskota, Grzegorz W. Wasilkowski: The Exact Exponent of Sparse Grid Quadratures in the Weighted Case. J. Complexity 17(4): 840-849 (2001)
2000
11EELeszek Plaskota: The exponent of discrepancy of sparse grids is at least 2.1933. Adv. Comput. Math. 12(1): 3-24 (2000)
10EEMark A. Kon, Leszek Plaskota: Information complexity of neural networks. Neural Networks 13(3): 365-375 (2000)
1996
9EELeszek Plaskota: How to Benefit from Noise. J. Complexity 12(2): 175-184 (1996)
8EELeszek Plaskota: Worst Case Complexity of Problems with Random Information Noise. J. Complexity 12(4): 416-439 (1996)
1995
7EELeszek Plaskota: Average Complexity for Linear Problems in a Model with Varying Information Noise. J. Complexity 11(2): 240-264 (1995)
1993
6EEBoleslaw Z. Kacewicz, Leszek Plaskota: The Minimal Cost of Approximating Linear Operators Using Perturbed Information-The Asymptotic Setting. J. Complexity 9(1): 113-134 (1993)
5EELeszek Plaskota: A Note on Varying Cardinality in the Average Case Setting. J. Complexity 9(4): 458-470 (1993)
1992
4EELeszek Plaskota: Function approximation and integration on the wiener space with noisy data. J. Complexity 8(3): 301-323 (1992)
1991
3EEBoleslaw Z. Kacewicz, Leszek Plaskota: Noisy information for linear problems in the asymptotic setting. J. Complexity 7(1): 35-57 (1991)
1990
2EELeszek Plaskota: On average case complexity of linear problems with noisy information. J. Complexity 6(2): 199-230 (1990)
1989
1EELeszek Plaskota: Asymptotic error for the global maximum of functions in s dimensions. J. Complexity 5(3): 369-378 (1989)

Coauthor Index

1Piotr Gajda [15]
2Boleslaw Z. Kacewicz [3] [6] [23]
3Mark A. Kon [10] [13] [19] [20] [21]
4Youming Li [15]
5Andrzej W. Przybyszewski [20] [21]
6Klaus Ritter [14] [16] [17]
7Ian H. Sloan [22]
8Grzegorz W. Wasilkowski [12] [14] [15] [17] [23]

Copyright © Sun May 17 03:24:02 2009 by Michael Ley (ley@uni-trier.de)