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The paper achieved the application of rough set-SVM model in the prediction of supply chain performance evaluation. Firstly, the paper rejected redundant factors and extracted key factors by use of rough set theory; then, safe class of supply chain performance evaluation was gained based on the key factors which have achieved with the method of SVM (support vector machines). In the end, result of...
Multi-class classifier is usually constructed by means of combining the outputs of several binary ones, according to an error correcting output code (ECOC) scheme. In the paper, within the framework of ECOC, we analyse of the ECOC of kernel machines originally proposed before. Then we present the generalization bounds of the ECOC of kernel machines according to the results of stability and generalization...
A new technique developed at ESPCI ParisTech should allow cellular received signal strength fingerprints to play an important role in localization systems for regions that are not well covered by GPS. The article describes the ARPEGEO project, initiated to evaluate the impact of full-band GSM fingerprints analyzed with modern machine learning techniques. Results on indoor localization, as well as...
We propose a sparse probabilistic learning approach for nonlinear channel equalization in wireless communication systems, by using the relevant vector machine (RVM) technique. In particular, we propose two versions of the RVM based equalizer: 1) maximum a posterior RVM (MAP-RVM), 2) marginalized RVM (MRVM). Compared to the standard support vector machine (SVM) method, the proposed RVM approach not...
In email networks, user behaviors affect the way emails are sent and replied. While knowing these user behaviors can help to create more intelligent email services, there has not been much research into mining these behaviors. In this paper, we investigate user engagingness and responsiveness as two interaction behaviors that give us useful insights into how users email one another. Engaging users...
Human-computer interaction is moving towards giving computers the ability to adapt and give feedback in accordance to a user's emotion. Studies on emotion recognition show that combining face and voice signals produce higher recognition rates compared to using either one individually. In addition, majority of the emotion corpus used on these systems were modeled based on acted data with actors who...
Laughter is one important aspect when it comes to non-verbal communication. Though laughter is often associated with the feeling of happiness, it may not always be the case; laughter may also portray different kinds of emotions. We infer that a variety of other emotions exist during laughter and occurrence and therefore investigate this phenomenon. It is the objective of this research to be able to...
Cosmetics is necessary for everyone's daily live, its impact on economy can not be ignored, but severe inventory stacking and lacking problems still exist. However, the occurrence of these problems is likely to be decreased via forecasting demand accurately. Thus, an Aggregated Forecast Through Exponential Re-weighting-Improved Quantum Evolutionary Algorithm (AFTER-IQEA) forecasting model is developed...
The phenomenal success of social networking sites, such as Facebook, Twitter and LinkedIn, has revolutionized the way people communicate. This paradigm has attracted the attention of researchers that wish to study the corresponding social and technological problems. Link recommendation is a critical task that not only helps increase the linkage inside the network and also improves the user experience...
In this paper, a novel method for profiling phishing activity from an analysis of phishing emails is proposed. Profiling is useful in determining the activity of an individual or a particular group of phishers. Work in the area of phishing is usually aimed at detection of phishing emails. In this paper, we concentrate on profiling as distinct from detection of phishing emails. We formulate the profiling...
Prediction of regional logistics requirement provides a basis for the plan of regional logistics. In the study, support vector regression is presented to predict regional logistics requirement. The regional logistics data from 1996 to 2006 in Shanghai municipality are used as the application data of support vector regression. The comparison of prediction error between BP neural network and support...
The performance of a model, which is trained with offline data, is highly relied on the conditions in which the system is working. When the working conditions change, the prediction accuracy of the model will be reduced significantly. To solve this problem, we propose an adaptive SVR modeling method based on vector-field-smoothed (VFS) algorithm. This method can adapt the model quickly to new working...
Aiming at the shortages of the existing data-mining model for classification of customer's credit sale risk, a new classification model based on rough sets and support vector machine presents is put forward in this paper. First, the theory of rough set is applied to pick up and reduce the index attributes. Then, the training samples are sent to the support vector machine to train and learn. After...
Analysis of dissolved gases content in power transformer oil is very important to monitor transformer latent fault and ensure normal operation of entire power system. Analysis of dissolved gases content in power transformer oil is a complicated problem due to its nonlinearity and the small quantity of training data. Support vector machine (SVM) has been successfully employed to solve classification...
Support vector machine has a wide range of applications in the communications signal modulation recognition, its parameters directly affect the recognition results, but lack of proper selection methods. In this paper, the simulated annealing algorithm has been utilized for optimization of the parameters C and g of support vector machine classifier. Compared with genetic algorithm, which is a traditional...
In order to solve the problem of BP neural network, genetic support vector regression is presented to predict the lifetime of cylinder. Support vector regression (SVR) is a novel prediction algorithm based on structure risk minimization principles, which can lead to great generalization ability. In the genetic support vector regression model, the genetic algorithm is used to optimize the parameters...
The problem of end effects in Hilbert-Huang transform is produced in the Empirical Mode Decomposition (EMD), which has a badly effect on Hilbert-Huang transform. In order to overcome this problem, multi-objective Genetic Algorithm (GA) for solving the parameters selection of RBF Neural Network (RBF_NN) (GRHHT) is presented in this paper. Then the RBF_NN is used to predict the signal before EMD. The...
Network is more and more popular in the present society. Least squares support vector machine is a kind modified support vector machine for classification, which can solve a convex quadratic programming problem. Least squares support vector machine is presented to network intrusion detection. We apply KDDCUP99 experimental data of MIT Lincoln Laboratory to research the classification performance of...
Aimed at the quantitative analysis of pulverized coal ignition temperature, this paper presents a piecewise least squares support vector machine modeling method, where several sub-models are created according to the burning characteristics of lignite, bituminous coal, lean coal and anthracite coal etc. and the parameters of each sub-model are optimized independently. By implementing the piecewise...
In this paper, because the induction machines (IM) are described as the plants of highly nonlinear and parameters time-varying, to obtain excellent control performances of IM and overcome the shortcomings of the fast modified variable metric optimal learning algorithm (MDFP) and back propagation (BP) learning algorithm of neural network, such as requiring derivation in the process of learning and...
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