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Recently several methods have been proposed to learn from data that are represented as sets of multidimensional vectors. Such algorithms usually suffer from the high demand of computational resources, making them impractical on large-scale problems. We propose to solve this problem by condensing i.e. reducing the sizes of the sets while maintaining the learning performance. Three methods are examined...
We introduce a new discriminative learning method for image classification. We assume that the images are represented by unordered, multi-dimensional, finite sets of feature vectors, and that these sets might have different cardinality. This allows us to use consistent nonparametric divergence estimators to define new kernels over these sets, and then apply them in kernel classifiers. Our numerical...
This paper describes a method for traffic signs detection, tracking and recognition. Color and shape are combined to detect signs. Hue and saturation are used to detect the red color of the sign. Circles are detected through the improved round-degree method of extracting area feature parameters. An improved Kalman filter is introduced to track multiple targets in the next frames. A feature extraction...
Feature extraction from vibration signals has been investigated extensively over the past decades as a key issue in machine condition monitoring and fault diagnosis. Most existing methods, however, assume a linear model of the underlying dynamics. In this study, the feasibility of devoting nonlinear dynamic parameters to characterizing bearing vibrations is studied. Firstly, fuzzy sample entropy (FSampEn)...
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