A
Adam Krzyżak
Researcher at Concordia University
Publications - 264
Citations - 8136
Adam Krzyżak is an academic researcher from Concordia University. The author has contributed to research in topics: Support vector machine & Radial basis function network. The author has an hindex of 37, co-authored 244 publications receiving 7631 citations. Previous affiliations of Adam Krzyżak include West Pomeranian University of Technology & Wrocław University of Technology.
Papers
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Methods of combining multiple classifiers and their applications to handwriting recognition
TL;DR: On applying these methods to combine several classifiers for recognizing totally unconstrained handwritten numerals, the experimental results show that the performance of individual classifiers can be improved significantly.
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Rival penalized competitive learning for clustering analysis, RBF net, and curve detection
Lei Xu,Adam Krzyżak,Erkki Oja +2 more
TL;DR: Experimental results show that RPCL outperforms FSCL when used for unsupervised classification, for training a radial basis function (RBF) network, and for curve detection in digital images.
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Learning and design of principal curves
TL;DR: This work defines principal curves as continuous curves of a given length which minimize the expected squared distance between the curve and points of the space randomly chosen according to a given distribution, making it possible to theoretically analyze principal curve learning from training data and it also leads to a new practical construction.
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On the Strong Universal Consistency of Nearest Neighbor Regression Function Estimates
TL;DR: It is shown that all modes of convergence in L 1 are equivalent if the regression variable is bounded and under the additional condition k/log n → ∞ the strong universal consistency of the estimate is obtained.
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Piecewise linear skeletonization using principal curves
Balázs Kégl,Adam Krzyżak +1 more
TL;DR: The results indicated that the proposed algorithm can find a smooth medial axis in the great majority of a wide variety of character templates and that it substantially improves the pixel-wise skeleton obtained by traditional thinning methods.