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ORCIDPrzemyslaw Klesk
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2020 – today
- 2024
[j8]Przemyslaw Klesk
:
FastRealBoostBins: An ensemble classifier for fast predictions implemented in Python via numba.jit and numba.cuda. SoftwareX 26: 101644 (2024)- [c23]Aneta Bera, Dariusz Sychel, Przemyslaw Klesk:
Rotationally Invariant Object Detection on Video Using Zernike Moments Backed with Integral Images and Frame Skipping Technique. ICCS (2) 2024: 68-83 - [c22]Dariusz Sychel, Aneta Bera, Przemyslaw Klesk:
Cascade Training as a Tree Search with Dijkstra's Algorithm. ICCS (2) 2024: 251-265 - 2021
- [j7]Przemyslaw Klesk, Marcin Korzen:
Can Boosted Randomness Mimic Learning Algorithms of Geometric Nature? Example of a Simple Algorithm That Converges in Probability to Hard-Margin SVM. IEEE Trans. Neural Networks Learn. Syst. 32(9): 3798-3818 (2021) - [c21]Przemyslaw Klesk:
Logarithmic Least Squares criterion revisited for general matrices of pairwise comparisons. KES 2021: 148-157 - [c20]Przemyslaw Klesk, Aneta Bera, Dariusz Sychel:
Extended Zernike invariants backed with complex-valued integral images for detection tasks. KES 2021: 357-368 - 2020
- [c19]Dariusz Sychel, Przemyslaw Klesk, Aneta Bera:
Relaxed Per-Stage Requirements for Training Cascades of Classifiers. ECAI 2020: 1523-1530 - [c18]Dariusz Sychel, Przemyslaw Klesk, Aneta Bera:
Branch-and-Bound Search for Training Cascades of Classifiers. ICCS (4) 2020: 18-34 - [c17]Przemyslaw Klesk, Aneta Bera, Dariusz Sychel:
Reduction of Numerical Errors in Zernike Invariants Computed via Complex-Valued Integral Images. ICCS (3) 2020: 327-341
2010 – 2019
- 2019
- [j6]Aneta Bera, Przemyslaw Klesk, Dariusz Sychel:
Constant-Time Calculation of Zernike Moments for Detection with Rotational Invariance. IEEE Trans. Pattern Anal. Mach. Intell. 41(3): 537-551 (2019) - [c16]Przemyslaw Klesk:
From Synthetic Images Towards Detectors of Real Objects: A Case Study on Road Sign Detection. ICAISC (2) 2019: 14-26 - 2018
- [j5]Przemyslaw Klesk, Mariusz Kapruziak, Bogdan Olech:
Statistical moments calculated via integral images in application to landmine detection from Ground Penetrating Radar 3D scans. Pattern Anal. Appl. 21(3): 671-684 (2018) - [c15]Przemyslaw Klesk, Aneta Bera:
Constant-time Extraction of Statistical Moments for Object Detection Procedures. ICPRAM 2018: 49-59 - [c14]Dariusz Sychel, Przemyslaw Klesk, Aneta Bera:
Notes on Expected Computational Cost of Classifiers Cascade: A Geometric View. ICPRAM 2018: 103-114 - 2017
- [c13]Przemyslaw Klesk:
Constant-Time Fourier Moments for Face Detection - Can Accuracy of Haar-Like Features Be Beaten? ICAISC (1) 2017: 530-543 - 2016
- [c12]Przemyslaw Klesk:
Quick Real-Boost with: Weight Trimming, Exponential Impurity, Bins, and Pruning. ICAISC (1) 2016: 597-609 - [c11]Przemyslaw Klesk, Mariusz Kapruziak, Bogdan Olech:
Fast Extraction of 3D Fourier Moments via Multiple Integral Images: An Application to Antitank Mine Detection in GPR C-Scans. ICCVG 2016: 206-220 - 2015
- [j4]Przemyslaw Klesk, Andrzej Godziuk, Mariusz Kapruziak, Bogdan Olech:
Fast Analysis of C-Scans From Ground Penetrating Radar via 3-D Haar-Like Features With Application to Landmine Detection. IEEE Trans. Geosci. Remote. Sens. 53(7): 3996-4009 (2015) - [c10]Przemyslaw Klesk, Mariusz Kapruziak, Bogdan Olech:
A Comparison of Shallow Decision Trees Under Real-Boost Procedure with Application to Landmine Detection Using Ground Penetrating Radar. ICAISC (1) 2015: 436-447 - 2014
- [c9]Przemyslaw Klesk, Mariusz Kapruziak, Bogdan Olech:
Boosted Classifiers for Antitank Mine Detection in C-Scans from Ground-Penetrating Radar. ACS 2014: 11-25 - 2013
- [j3]Marcin Korzen, Szymon Jaroszewicz, Przemyslaw Klesk:
Logistic regression with weight grouping priors. Comput. Stat. Data Anal. 64: 281-298 (2013) - [c8]Przemyslaw Klesk, Andrzej Godziuk, Mariusz Kapruziak, Bogdan Olech:
Landmine Detection in 3D Images from Ground Penetrating Radar Using Haar-Like Features. ICAISC (1) 2013: 559-567 - 2012
- [c7]Przemyslaw Klesk:
Probabilistic Estimation of Vapnik-Chervonenkis Dimension. ICAART (1) 2012: 262-270 - [c6]Przemyslaw Klesk:
A Comparison of Complexity Selection Approaches for Polynomials Based on: Vapnik-Chervonenkis Dimension, Rademacher Complexity and Covering Numbers. ICAISC (2) 2012: 100-110 - [c5]Marcin Korzen, Przemyslaw Klesk:
Sample Complexity of Linear Learning Machines with Different Restrictions over Weights. ICAISC (2) 2012: 111-119 - 2011
- [j2]Przemyslaw Klesk, Marcin Korzen:
Sets of approximating functions with finite Vapnik-Chervonenkis dimension for nearest-neighbors algorithms. Pattern Recognit. Lett. 32(14): 1882-1893 (2011) - [c4]Przemyslaw Klesk:
A Relationship between Cross-validation and Vapnik Bounds on Generalization of Learning Machines. ICAART (1) 2011: 5-17 - [c3]Przemyslaw Klesk:
Probabilistic Connection between Cross-Validation and Vapnik Bounds. ICAART (Revised Selected Papers) 2011: 31-55 - 2010
- [j1]Przemyslaw Klesk:
Probabilities of discrepancy between minima of cross-validation, Vapnik bounds and true risks. Int. J. Appl. Math. Comput. Sci. 20(3): 525-544 (2010) - [c2]Przemyslaw Klesk, Antoni Wilinski:
Market Trajectory Recognition and Trajectory Prediction Using Markov Models. ICAISC (1) 2010: 405-413
2000 – 2009
- 2008
- [c1]Marcin Korzen, Przemyslaw Klesk:
Maximal Margin Estimation with Perceptron-Like Algorithm. ICAISC 2008: 597-608 - 2005
- [p2]Przemyslaw Klesk:
Algorithm for Automatic Definition of Validated and Non-Validated Region in Multi-Dimensional Space. Enhanced Methods in Computer Security, Biometric and Artificial Intelligence Systems 2005: 361-370 - [p1]Przemyslaw Klesk:
The Jeep Problem, searching for the best strategy with a genetic algorithm. Information Processing and Security Systems 2005: 453-464
Coauthor Index
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last updated on 2024-07-20 20:27 CEST by the dblp team
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