2020 - Fellow of the International Association for Pattern Recognition (IAPR) For contributions to statistical pattern recognition and machine learning, and for service to IAPR
2012 - IEEE Fellow For contributions to nonparametric algorithms and classification systems for machine learning
His primary areas of investigation include Artificial intelligence, Pattern recognition, Statistics, Kernel regression and Radial basis function. He works mostly in the field of Artificial intelligence, limiting it down to topics relating to Machine learning and, in certain cases, Bayesian probability, as a part of the same area of interest. His research integrates issues of Image processing and Numeral system in his study of Pattern recognition.
His Statistics research is multidisciplinary, incorporating elements of Bounded function and Applied mathematics. Adam Krzyżak combines subjects such as Rate of convergence, Pointwise, Random variable and Control theory with his study of Kernel regression. His Handwriting recognition study integrates concerns from other disciplines, such as Segmentation, Curvature, Classifier and Binary image.
Adam Krzyżak spends much of his time researching Artificial intelligence, Pattern recognition, Applied mathematics, Statistics and Rate of convergence. His Artificial intelligence research includes themes of Machine learning and Computer vision. Adam Krzyżak regularly ties together related areas like Numeral system in his Pattern recognition studies.
Adam Krzyżak has included themes like Nonparametric statistics, Radial basis function, Regression, Bounded function and Nonlinear system in his Applied mathematics study. His Rate of convergence research is multidisciplinary, relying on both Smoothing, Estimator and Mathematical optimization. His work deals with themes such as Algorithm and Radial basis function network, which intersect with Mathematical optimization.
Adam Krzyżak mainly focuses on Artificial intelligence, Pattern recognition, Artificial neural network, Rate of convergence and Deep learning. He has researched Artificial intelligence in several fields, including Logarithm and Nonparametric regression. He merges Pattern recognition with Epileptic seizure in his research.
The various areas that Adam Krzyżak examines in his Artificial neural network study include Transfer of learning, Paraphrase, Natural language processing and Breast cancer. His Rate of convergence research incorporates elements of Uncertainty quantification, Density estimation, Imperfect, Mathematical optimization and Function. His Classifier study combines topics from a wide range of disciplines, such as Computational complexity theory, Data decomposition, Boosting and Support vector machine.
His primary areas of study are Artificial intelligence, Data mining, Pattern recognition, Rate of convergence and Algorithm. His Artificial intelligence research incorporates themes from Nonparametric regression and Least squares. His Data mining research is multidisciplinary, incorporating perspectives in Silhouette, Validity assessment and Cluster analysis.
Adam Krzyżak undertakes interdisciplinary study in the fields of Pattern recognition and Epileptic seizure through his works. His Rate of convergence course of study focuses on Function and Quantile, Statistics, Importance sampling, Combinatorics and Random variable. His research in Algorithm intersects with topics in Classifier, Support vector machine and Decision tree.
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Methods of combining multiple classifiers and their applications to handwriting recognition
L. Xu;A. Krzyzak;C.Y. Suen.
systems man and cybernetics (1992)
Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
L. Xu;A. Krzyzak;E. Oja.
IEEE Transactions on Neural Networks (1993)
Learning and design of principal curves
B. Kegl;A. Krzyzak;T. Linder;K. Zeger.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2000)
On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates
Luc Devroye;Laszlo Gyorfi;Adam Krzyzak;Gabor Lugosi.
Annals of Statistics (1994)
Fast SVM training algorithm with decomposition on very large data sets
Jian-xiong Dong;A. Krzyzak;C.Y. Suen.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2005)
Piecewise linear skeletonization using principal curves
B. Kegl;A. Krzyzak.
IEEE Transactions on Pattern Analysis and Machine Intelligence (2002)
Image denoising using neighbouring wavelet coefficients
G. Y. Chen;T. D. Bui;A. Krzyzak.
Computer-Aided Engineering (2005)
Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies
Pawel Filipczuk;Thomas Fevens;Adam Krzyzak;Roman Monczak.
IEEE Transactions on Medical Imaging (2013)
On radial basis function nets and kernel regression: statistical consistency, convergence rates, and receptive field size
Lei Xu;Lei Xu;Adam Krzyżak;Alan Yuille.
Neural Networks (1994)
Distribution-Free Pointwise Consistency of Kernel Regression Estimate
Wlodzimierz Greblicki;Adam Krzyzak;Miroslaw Pawlak.
Annals of Statistics (1984)
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