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Sentiment analysis is a technology with great practical value, it can solve the phenomenon of network comment information disorderly to a certain extent, and accurate positioning of user information required. Currently for Chinese sentiment analysis research is relatively small, including a variety of supervised learning method of classification result and the text feature representation methods and...
The standard support vector machine (SVM) is a common method of machine learning, the parameters selection of SVM affects the machine learning ability directly. At present, the research on the choice of SVM parameters is still no uniform approach. In order to avoid the difficult problem of selecting parameters, this paper used a deformed SVM, that is, v-SVM, selected parameters of v-SVM by particle...
In [1], the boundeness of one dimensional maximal operator of dyadic derivative is discussed. Unfortunately, the proof is uncorrect. In this paper, we consider the maximal operator of dyadic derivative on Vilenin group. With the help of counter-example we prove that the maximal operator is not bounded from the Hardy space Hq to the Hardy space Hq for 0 <; q ≤ 1.
In the noisy environment, the performance of speech recognition system may become worse to some extent. In order to solve this problem, this paper used the zero-crossings with peak amplitudes (ZCPA) features as speech feature parameters, which are based on human hearings property. The extraction method of ZCPA features is that calculating the unward zero-crossing rate of speech signal gets frequency...
Kernel parameters selection of support vector machine is a very important problem, which has great influence on the performance of support vector machine. In order to improve the learning and generalization ability of support vector machine and enhance speech recognition system accuracy, a method of searching for the Gaussian kernel support vector machine optimal parameters(C, ?? ) based on particle...
Parameters selection of support vector machine is a very important problem, which has great influence on the performance of support vector machine. Particle swarm optimization is an efficient algorithm and it is broadly used in many research areas like pattern recognition and so on. In order to improve the learning and generalization ability of support vector machine, a method for searching the optimal...
Let Gp be the p-series field. In this paper we give the expansion of Fejer kernel with respect to the generalized orthonormal system of p-series field in the Kaczmarz rearrangement.As a consequence, we prove the Fejer kernel functions are uniformly integrable.
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