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.
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Journal ArticleDOI
Fast SVM training algorithm with decomposition on very large data sets
TL;DR: The results show that the proposed algorithm has a much better scaling capability than Libsvm, SVM/sup light/, and SVMTorch and the good generalization performances on several large databases have also been achieved.
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Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies
TL;DR: The results presented in this paper demonstrate that a computerized medical diagnosis system based on the analysis of cytological images of fine needle biopsies to characterize theseBiopsies as either benign or malignant would be effective, providing valuable, accurate diagnostic information.
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On radial basis function nets and kernel regression: statistical consistency, convergence rates, and receptive field size
TL;DR: In this paper, the convergence rate of RBF nets with respect to the number of hidden units is investigated and the existence of a consistent estimator for RBF networks is proven constructively.
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Distribution-Free Pointwise Consistency of Kernel Regression Estimate
TL;DR: In this paper, an estimate of the convergence probability of a sequence of independent pairs of random variables distributed as a pair (X, Y) to the regression (E\{Y\mid X = x = x\}$ as $n$ tends to infinity in probability for almost all distributions.
Proceedings ArticleDOI
Image denoising using neighbouring wavelet coefficients
TL;DR: Experimental results show that NeighShrink is better than the Wiener filter and the conventional wavelet denoising approaches: Visu Shrink and SUREShrink, and different neighbourhood sizes are investigated and it is found that a size of 3/spl times/3 is the best among all window sizes.