Competitive and online piecewise linear classification
Author
Özkan, Hüseyin
Donmez, M.A.
Pelvan O.S.
Akman, A.
Kozat, Süleyman S.
Date
2013Source Title
2013 IEEE International Conference on Acoustics, Speech and Signal Processing
Publisher
IEEE
Pages
3452 - 3456
Language
English
Type
Conference PaperItem Usage Stats
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Show full item recordAbstract
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
ClassificationCompetitive
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)