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Factor analysis is mainly by extracting the compact representations of speakers' utterances, which are referred to as i-vectors. A low new space called total variability space, which is speaker and channel dependent is trained in the modeling. During the experiments, channel compensation approaches are used to remove the interference included by i-vectors. They are respectively are Nuisance Attribute...
The class label of each feature vector in the dataset is respectively added in the corresponding feature vector as a feature value, which build a new vector called altered feature vector, all of which compose the altered dataset. It is demostrated that an SVM based on the altered dataset has advantages such as high generilization performance and little structure risk, compared with an SVM based on...
Semiparametric detection consists of combining the statistical optimality of a parametric test to the robustness regarding the data of a nonparametric test. This approach is specially interesting in presence of statistical hypotheses depending on unknown probability distributions. The proposed semiparametric approach consists of splitting the measurement vector into two parts such that the first part...
This paper proposes a novel analog test generation based on the SVM (support vector machine) with a multitraining sets method. The test generation method in this paper generates test signals directly from the sample space of the output responses of the analog DUT. The training sets are randomly generated in the test generation, so we use a multitraining sets method to avoid the influence of choosing...
We present results from a comparative empirical study of two methods for constructing support vector machines (SVMs). The first method is the conventional one based on the quadratic programming approach, which builds the optimal separating hyperplane maximizing the margin between two classes (SVM-Q). The second method is based on the linear programming approach suggested by Vapnik to build a separating...
Support vector machine (SVM) is a widely used tool in classification problem. SVM solves a quadratic optimization problem to decide which instances of training dataset are support vectors, i.e., the necessarily informative instances to form the classifier. The support vectors are intact tuples taken from the training dataset. Releasing the SVM classifier to public use or shipping the SVM classifier...
For accelerating the training speed of support vector machines (SVM), a novel ldquomulti-trifurcate cascade (MTC)rdquo architecture was proposed in this paper, which held the advantages of fast feedback, high utilization rate of nodes, and more feedback support vectors. Then, a parallel algorithm for training SVM was designed based on the MTC architecture, and it was proven to converge to the optimal...
Port state control (PSC) inspection is the most important mechanism to ensure world marine safe. Recently, some SVM-based risk assessment systems have been presented in the world. They estimate the risk of each candidate ship based on its generic factors and history inspection factors to select high-risk one before conducting on-board PSC inspection. However, how to improve the performance of the...
Categorization of scenes is a fundamental process of human vision that allows us to efficiently and rapidly analyze our surroundings. Scene classification, the classification of images into semantic categories (e.g., coast, mountains, highways and streets) is a challenging and important problem nowadays. This paper is classifying the scenes using support vector machine with radial basis kernel with...
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