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The support vector machine has been recently developed for blind equalization of constant modulus signals. In this paper we propose to use a v-support vector regressor (nu-SVR) for blindly equalizing multipath channels because of the high generalization ability of the SVR for short burst sequences. A weighted least square procedure is presented for solving the blind nu-SVR equalizer. The performance...
This paper proposes a novel hierarchical speaker identification method to save the speaker identification and training time, viz. First is to get a coarse decision by a fast scan all registered speakers using PCA classifier to found M possible target speakers; then is to get a final decision by the proposed Multi-Reduced Support Vector Machine (MRSVM). And the MRSVM has two reduction steps to reduce...
The design approach for classifying the backend features of the PPRLM (Parallel Phone Recognition and Language Modeling) system is demonstrated in this paper. A variety of features and their combinations extracted by language dependent recognizers were evaluated based on the National Institute of Standards and Technology (NIST) Language Recognition Evaluation (LRE) 2003 corpus. Three well-known classifiers:...
SVM is a novel type of statistical learning methods that has been successfully used in speaker recognition. However, training SVM consumes long computing time and large memory with all training data. This paper proposes a speaker identification method based on multi- reduced support vector machine (MRSVM). MRSVM has two reduction steps. Firstly, speech feature dimensions are reduced by using KL transform,...
Support vector machines (SVM) is a novel machine learning method based on small-sample statistical learning theory (SLT), and is powerful for the problem with small sample, nonlinearity, high dimension, and local minima.SVM have been very successful in pattern recognition ,fault diagnoses and function estimation problems. Least squares support vector machines (LS-SVM) is an SVM version which involves...
We report results for classification of representations of music, spoken words, and text documents. Experimental comparisons with other state-of-the-art algorithms yield improved results for all three examples. We use a support vector machine (SVM) as our classifier in all experiments. This is driven by a kernel matrix of similarity measures between the sequences. Our similarity measure is based on...
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