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In this paper, we focused on the problem of automatic modulation classification of digital signals. Several useful characteristic parameters which can be used for modulation analysis are extracted from spectral correlation, for different types of modulated signals have different power spectral density functions. A density estimation approach based on Support Vector Machine (SVM) is developed. Also,...
Along with the increase number of users for the credit, the screening of applicants becomes very significant. If the credit of applicants is bad, the bank will obtain a great loss. Support vector machine (SVM) is one of the most popular kinds of algorithms for the new consumer's credit approval. However, there is a disadvantage that the more close to the optimal hyper plane, the greater possibility...
This paper expands the standard pronunciation space (SPS) to include pronunciation errors for automatic pronunciation error detection (APED), uses HMMs to represent the different distributions of pronunciation errors, proposes an adaptive unsupervised clustering of pronunciation errors based on the similarity measures between two HMMs, and then refines more detailed acoustic models for APED within...
With the rapid developments and extensive applications of internet, a large number of duplicated entities on the Web, especially on the Deep Web, require to be eliminated and integrated effectively. So identifying the corresponding entities on the Deep Web is critical. Due to the query interface on the HTML page represents the schema of the Web database, we firstly try to obtain the schema of the...
This paper proposes a novel parameter learning algorithm for a self-constructing fuzzy neural network (SCFFN) design. It concludes dynamic prior adjustment (DPA) which is employed to adjust parameters according to the distribution of the input samples and group-based symbiotic evolution (GSE) which is applied to train all the free parameters for the desired outputs. DPA considers the relevance between...
For the feature selection and parameter optimization of LS-SVM, propose a At first, a population of particles (feature subsets) was randomly generated, then the features and parameters are optimized by PSO algorithm. The experiments on the UCI database indicate that the proposed method can efficiently find the suitable feature subsets and LS-SVM parameters. Also, comparison are made against GALS-SVM...
Money laundering (ML) is a serious crime which makes it necessary to develop detection methods in transactions. Some researches have been carried on, but the problem is not thoroughly solved. Aiming at the low detection rate of suspicious transaction at home and abroad in financial field, and with an analysis of radial basis function (RBF) neural network, we propose a radial basis function neural...
In this paper, in order to reduce the support vectors on a large scale data set, we train support vector machines which utilize the hyper-spheres as the training samples. By representing adjacent samples of the same class as hyper-spheres, the boundary location can be controlled both by the center and radius of the hyper-spheres. We demonstrate that the optimization problem in this condition can be...
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