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      • Faculty of Engineering
      • Department of Electrical and Electronics Engineering
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      Competitive and online piecewise linear classification

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      Author
      Özkan, Hüseyin
      Donmez, M.A.
      Pelvan O.S.
      Akman, A.
      Kozat, Süleyman S.
      Date
      2013
      Source Title
      2013 IEEE International Conference on Acoustics, Speech and Signal Processing
      Publisher
      IEEE
      Pages
      3452 - 3456
      Language
      English
      Type
      Conference Paper
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      Abstract
      In this paper, we study the binary classification problem in machine learning and introduce a novel classification algorithm based on the 'Context Tree Weighting Method'. The introduced algorithm incrementally learns a classification model through sequential updates in the course of a given data stream, i.e., each data point is processed only once and forgotten after the classifier is updated, and asymptotically achieves the performance of the best piecewise linear classifiers defined by the 'context tree'. Since the computational complexity is only linear in the depth of the context tree, our algorithm is highly scalable and appropriate for real time processing. We present experimental results on several benchmark data sets and demonstrate that our method provides significant computational improvement both in the test (5 ∼ 35×) and training phases (40 ∼ 1000×), while achieving high classification accuracy in comparison to the SVM with RBF kernel. © 2013 IEEE.
      Keywords
      Classification
      Competitive
      Context tree
      LDA
      Online
      Piecewise linear
      Competitive
      Context tree
      LDA
      Online
      Piecewise linear
      Algorithms
      Classification (of information)
      Data mining
      Piecewise linear techniques
      Signal processing
      Trees (mathematics)
      Permalink
      http://hdl.handle.net/11693/27965
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/ICASSP.2013.6638299
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