PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Classification invariante en translation de signaux non stationnaires
Vincent Guigue, Alain Rakotomamonjy and Stéphane Canu
In: GRETSI 2005, 6-9 Sept. 2005, Louvain la neuve, Belgium.

This is the latest version of this eprint.

Abstract

This paper addresses the question of the classification of on-stationary signals. We make the hypothesis that each signal includes a pattern, the shape of which is discriminant but the time location of which is random and unknown. We propose to build a graphical representation, based on continuous wavelet transform in order to eliminate the absolute time reference of the description. Then, we define a dot product between graphs and compare the results of SVM and k-nn.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:1929
Deposited By:Vincent Guigue
Deposited On:30 December 2005

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