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The decision tree induction learning is a typical machine learning approach which has been extensively applied for data mining and knowledge discovery. For numerical data and mixed data, discretization is an essential pre-processing step of decision tree learning. However, when coping with big data, most of the existing discretization approaches will not be quite efficient from the practical viewpoint...
This paper presents a study on the prediction-based reversible data hiding for digital images. By adopting a diagonal structure for prediction, a large number of pixels in the host image can be predicted and further modified for data embedding. Two prediction schemes are derived from the same structure so that the performance with the different numbers of prediction errors can be compared. For either...
To effectively classify infrared spectrum (IRS) fingerprints of Chinese herbs, this paper presents a new radial basis function (RBF) network namely, Locally Gaussian Mixture based RBF (LGM-RBF) Network. Unlike the traditional RBF network, the LGM-RBF has a mix layer between the hidden layer and the output layer. The hidden nodes with spherical Gaussian are initially grouped so that each group is corresponding...
This paper proposes a new image retrieval method using non-separable discrete wavelets (NDWT) and local binary patterns (LBP). Compared with the traditional wavelet, the three high frequency sub-images generated by the non-separable wavelets can extract more information and do not extensively focus on the three special directions any more. Further, local image texture and their occurrence histogram...
The effect of introducing ultrasonic energy to reduce the bonding temperature and time as well as the void formation in state-of-the-art thermocompression bonding of die attach film (DAF)-laminated thin silicon dies on glass substrates is reported in this paper. Process studies are conducted using an in-house automated thermosonic bonding equipment equipped with a 40 kHz ultrasonic transducer. The...
We propose a market mechanism that can be implemented on clustering aggregation problem among selfish systems, which tend to lie about their correct clustering during aggregation process. Our study is the preliminary step toward the development of robust distributed data mining among selfish systems.
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