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Traditional SVM (support vector machine) multi-class classification methods are mainly based on one-to-one and one-to-multi, which both have disadvantages in applications: slow computational speed and low classification precision. This paper introduces a new method based on error correcting code to reduce the training time and improve the classification precision. In view of the relations among the...
According to an uncertain discrete system with input saturation, a new chattering free support vector regression sliding mode control (SVR-SMC) law based on Linear Matrix Inequalities (LMIs) is presented. The output of SVR is used for replacing sign function of the reaching law in traditional sliding mode control (SMC). An equivalent matrix is constructed for input saturation condition in the scheme...
To reduce the computational cost of the incremental learning, a fast SVM incremental learning algorithm based on the convex hulls algorithm is proposed in this paper. The given algorithm is based on utilizing the result of the previous training effectively and retaining the most important samples for the incremental learning to reduce the computational cost. In the process of incremental learning,...
This paper proposed a novel blind image watermark scheme in discrete cosine transform (DCT) domain. The original image is divided into small image blocks with size 8*8 first, and then the binary watermark bits are embedded into DCT domain of image blocks adaptively through a novel watermark embedding algorithm. The original image is not needed in the extraction algorithm. For gaining good fidelity...
An actual physical simulation model was constructed to simulate the course of oil and water migration. Under certain physical property conditions, we simulated the water injection well and the oil well on the physical simulation model, and continuous measured online the oil and water content of different area of model in three-dimensional space using the 512 routes resistivity measuring circuit, then...
The incorporation of prior knowledge into SVMs for classification is the key element that allows increasing the performance to many applications. Wu proposed weighted margin support vector machine (WMSVM), the scalability aspect of the approach to handle large data sets still needs much of exploration. In this paper, we describe a generalization of weighted margin multi-class core vector machine (WMMCVM)...
A novel control chart pattern recognition system using support vector machine(SVM) is presented. Pattern recognition techniques have been wildly applied to identify abnormal patterns in control charts. Abnormal patterns exhibited by such charts can be associated with certain assignable causes affecting the process. Most of the existing recognition method are capable of recognizing a single abnormal...
In traditional machine learning approaches to classification, one uses only a labelled set to train the classifier. Labelled instances however are often difficult, expensive, or time consuming to obtain, as they require the efforts of experienced human annotators. Meanwhile unlabeled data may be relatively easy to collect, but there has been few ways to use them. Semi-supervised learning addresses...
Some techniques have been applied to improving software quality by classifying the software modules into fault-prone or non fault-prone categories. This can help developers focus on some high risk fault-prone modules. In this paper, a distribution-based Bayesian quadratic discriminant analysis (D-BQDA) technique is experimental investigated to identify software fault-prone modules. Experiments with...
Pairwise coupling is a widely used method in multi-class SVM and max wins voting (MWV) strategy can obtain a global classification by considering each partial answer of binary classifier as vote. But MWV strategy has an important drawback, due to the nonsense caused by those meaningless binary classifier. This paper presents a novel approach, which considers the pairwise SVM classification as a decision-making...
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