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This paper presents a novel approach for pattern recognition in machine olfaction by constructing an ensemble classifier using a bionic olfactory neural network, namely KIII model, and support vector machine (SVM). In this approach, feature vectors are firstly processed by KIII model which stimulates information processing function of olfactory bulb, and then classified by SVM. In the experiment to...
Serial numbers identification of RMB (the name of Chinese paper currency) is a nonlinear and high dimensions pattern recognition problem which sample is limited. It is one of many difficulty problems in pattern recognition. It also has great research and practical value. This thesis studies the multi-class optimize algorithm in statistical learning theory, analyzes SMOD algorithm and its precondition...
Industrial equipments that employ element identification tend to be expensive as they utilize built-in spectroscopes and computers for post processing. In this paper we present an in situ fully automatic method for detecting constituent elements in a sample specimen using computer vision and machine learning techniques on Laser Induced Breakdown Spectroscopy (LIBS) spectra. This enables the development...
In order to improve the recognition rate of a sucker rod's defect and reduce the rapture possibility of the rod, the mixed characters include of wavelet packet energy character and the peak value in the time-domain were used as the input of a recognition network, and artificial neural networks (ANN) and support vector machines (SVM) were used and compared as the recognition network to get the best...
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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