L
Lei Xu
Researcher at Central South University
Publications - 638
Citations - 16882
Lei Xu is an academic researcher from Central South University. The author has contributed to research in topics: Computer science & Artificial neural network. The author has an hindex of 45, co-authored 518 publications receiving 13622 citations. Previous affiliations of Lei Xu include Dongguan University of Technology & Lappeenranta 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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A new curve detection method: randomized Hough transform (RHT)
Lei Xu,Erkki Oja,P. Kultanen +2 more
TL;DR: This work proposes a new method for curve detection that has the advantages of small storage, high speed, infinite parameter space and arbitrarily high resolution, and the preliminary experiments have shown that the new method is quite effective.
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On convergence properties of the em algorithm for gaussian mixtures
Lei Xu,Michael I. Jordan +1 more
TL;DR: The mathematical connection between the Expectation-Maximization (EM) algorithm and gradient-based approaches for maximum likelihood learning of finite gaussian mixtures is built up and an explicit expression for the matrix is provided.
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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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Randomized Hough transform (RHT): basic mechanisms, algorithms, and computational complexities
TL;DR: In this article, a new curve detection approach called the randomized Hough transform (RHT) was heuristically proposed by the authors, inspired by the efforts of using neural computation learning techniques for curve detection.