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High usability myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set on the performance of several pattern recognition algorithms during dynamic contractions. It is shown that combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static...
Support Vector Machine (SVM) is one of the state-of-the-art tools for linear and nonlinear pattern classification. One of the design issues in SVM classifier is reducing the number of support vectors without compromising the classification accuracy. In this paper, a novel technique which requires only a subset of the support vectors is proposed. The subset is obtained by including only those support...
As an effective tool in pattern recognition and machine learning, support vector machine (SVM) has been adopted abroad. In developing a successful SVM classifier, eliminating noise and extracting feature are very important. This paper proposes the application of kernel PCA to SVM for feature extraction. Then PSO Algorithm is adopted to optimization of these parameters in SVM. The novel time series...
Traditional Chinese Pulse Diagnosis (TCPD), one of the four diagnostic methods of Traditional Chinese Medicine (TCM), had been proved to be clinically valid in Chinese Medicine history. Different from most previous work which focused on the diagnosis of cardiovascular diseases, this paper further investigated the possibility of diagnosing cholecystitis and nephrotic syndrome using the pulse waveform...
Diagnosis of pathological voice is one of the most important issues in biomedical applications of speech technology. This study focuses on the classification of pathological voice using the HMM (hidden Markov model), the GMM (Gaussian mixture model) and a SVM (support vector machine), and then compares the results to work done previously using an ANN (artificial neural network). Speech data were collected...
The paper proposes a new multi-classifier for pattern recognition by combining neural network with SVM (support vector machine). The multi-classifier has the advantages of SVM and NN (neural network). According to the properties of Bragg peak, zero frequency disturbance and the target of moving with time-varying velocity among the echo signal of HFSWR (high frequency surface wave radar), the multi-classifier...
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