The nature of statistical learning theory
The nature of statistical learning theory
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Support vector machines and the multiple hypothesis test problem
IEEE Transactions on Signal Processing
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Since fusion plasma experiment generates hundreds of signals. In analyzing these signals it is important to have automatic mechanisms for searching similarities and retrieving of specific data in the waveform database. Wavelet transform (WT) is a transformation that allows to map signals to spaces of lower dimensionality, that is, a smoothed and compressed version of the original signal. Support vector machine (SVM) is a very effective method for general purpose pattern recognition. Given a set of input vectors which belong to two different classes, the SVM maps the inputs into a high-dimensional feature space through some non-linear mapping, where an optimal separating hyperplane is constructed. This hyperplane minimizes the risk of misclassification and it is determined by a subset of points of the two classes, named support vectors (SV). In this work, the combined use of WT and SVM is proposed for searching and retrieving similar waveforms in the TJ-II database. In a first stage, plasma signals will be preprocessed by WT in order to reduce the dimensionality of the problem and to extract their main features. In the next stage, and using the new smoothed signals produced by the WT, SVM will be applied to show up the efficency of the proposed method to deal with the problem of sorting out thousands of fusion plasma signals.