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With the increasing of vehicle quantity, traffic violations occur frequently. It is an efficient means to avoid traffic accident through controlling traffic violation. Based on the study of traffic accidents, black spot of traffic violation can be determined. But little research work has been done on this subject at present. This paper proposed a traffic violation black spot analysis method. In the...
Least Squares Support Vector Machines(LSSVM) regression principle and sparsity configuration were introduced. In this paper online dynamic modeling based on Sparse LSSVM(SLSSVM) was proposed for wood drying process with strong coupling and nonlinear characteristics. The sample data of Fraxinus mandshurica in the speed-down drying stage were gathered in the experiments of a downscaled industrial wood...
In the paper, ecological economy forecasting based on support vector machine is presented. The ecological economy forecasting is the forecasting of the three energy indices: self-sufficient rate of energy, environment load rate and production rate of pure energy, so the three energy indices are predicted here. The experimental data of the three energy indices: self-sufficient rate of energy, environment...
Because of the superiority on processing the uncertain information and fuzzy information, the uncertainty mathematical theory has been widely applied in fault diagnosis of complex system. In this paper, first, the combination of basic uncertainty mathematics theory and support vector machine (SVM) and its application in fault diagnosis are introduced in detail. Then, some of the key technologies are...
Visible and near infrared (NIR) spectroscopy was utilized to determine the growing areas of Tremella fuciformis. Principal component analysis (PCA) obtained the cluster plot which shows the difficulty to determine the growing area by the first three principal components. Least-square support vector machine (LS-SVM) was used to establish the calibration model. Successive projections algorithm (SPA)...
In this paper, a novel fuzzy support vector machine based image watermarking scheme is proposed.Since the application of support vector machine in the process of watermarking technology is only a simple classification of the image. However,the fuzzy support vector machines by selecting the appropriate degree of membership to reflect the different importance of the different sample points. In this...
The problem of recognizing multiple object classes in natural images has proven to be a difficult challenge for compute vision. It is reasonable to look to biology for inspiration, a novel multiclass object recognition algorithm based on a biologically inspired model named ST model is proposed. ST model is based on the theory of biological neurology, which calculates object features that exhibit position...
Transductive SVM is a semi-supervised method, which can capture the intrinsic properties of each class' structure in feature space with the help of large number of unlabeled data. It can optimize the classification effect with little and poor representative labeled samples. A weakness of this method is one need determine the number of unlabeled samples which belongs to a specific class before iteration,...
The support vector machines (SVMs), as one of special regularization methods, has been used successfully in the field of pattern recognition. However, the traditional SVMs, a supervised learning method, gets the normal vector of the decision boundary mainly according to the largest interval law but has not taken the underlying geometric structure and the discriminant information into full consideration...
The prediction of software defect-fixing effort is important for strategic resource allocation and software quality management. Machine learning techniques have become very popular in addressing this problem and many related prediction models have been proposed. However, almost every model today faces a challenging issue of demonstrating satisfactory prediction accuracy and meaningful prediction results...
To help handle battlefield information superiority to decision superiority (i.e. to rapidly arrive at better decisions than adversaries can respond to), many scientific, technical and technological challenges must be addressed. The most critical of those are information fusion and management at different levels, communication. This paper decribes battlefield information as data streams and mining...
For improving accuracy and robust property of human detection, fusion of image sequences captured from visible-thermal sensors is lucrative. Instead of performing it in pixel-level directly, we try to fuse object features by a novel image sequence fusion algorithm based on gradient feature (GFIF). The GFIF algorithm calculate gradients of input images to form a joint histograms of Oriented Gradient...
Text classification has gained booming interest over the past few years. In this paper we look at the main approaches that have been taken towards text classification. The key text classification techniques including text model, feature selection methods and text classification algorithms are discussed. This work focus on the implementation of a text classification system based on Mutual Information...
As China's continuous improvement in the degree of information, the wireless communication network has covered all regions of the country, and information has also brought a great deal of information network security issues. In this paper, firstly, the information security emergency response procedures and disposal system are proposed according to the comprehensive analysis of weak links in information...
In the few years, several neural networks are proposed to image classification. Support vector machine classifier employs the structural risk minimization principles, which make support vector machine classifier have good generalization ability. In order to solve the problem of parameters selection of support vector machine, particle swarm optimization is applied to select the parameters of support...
In recent years high-resolution space borne images have disclosed a large number of new opportunities for medium and large-scale rubber plant mapping. Some traditional algorithms used for hyper spectral remote sensing image classification have some problems such as low computing rate, low accuracy. According to SVM theory, the Rubber plant classification model based on SVM was constructed, by experimenting...
In order to establish 3D solid model in geological fields, the key question is to obtain complete ore grade attribute data. Traditional forecasting methods such as neural networks, support vector machine (SVM) are adopted frequently. However, these methods are lack of necessary probability information and can not acquire the uncertainty of forecasts. In this paper, a new forecasting model is proposed...
Assessment for computer network attack is a technology for assessing hostile attacks against computer network system. In the paper, relevance vector machine is applied to assessment for computer network attack. Compared with support vector machine assessment method, the RVM method can provide better classification accuracy. The experimental data collected in the paper include U2R attack, R2L attack,...
In the paper, support vector machine is adopted to predict the time delay induced in the networked control system by using time delay historical data. We employ the reaction curve of sine to testify the feasibility of support vector machine. Finally, BP neural network delay predictor is used to compare with support vector machine delay predictor. The testing results demonstrate that the prediction...
Slope stability is always a very complex issue in engineering. Base on the theoretical analysis of BP neural network and support vector machine (SVM), some major factors which influence the slope stability are selected in soil slope and the slope samples are trained and identified. The identification rates of BP neural network and SVM both achieved 100%. In identification precision and elapsed time,...
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