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High pressure dissolving (HPD) is a very important process for alumina production. During HPD process, alumina caustic ratio (ACR) of the dissolved slurry is a very important economic technical indicator. In practice, there are many factors influencing ACR and there are different noise levels for different HPD conditions. So, it is very difficult to predict ACR with single model accurately. In this...
Self-adaptive back propagation neural network (BPNN) models based on hierarchical clustering were developed to classify corn kernels. To generate the sample sets, randomly selected kernels were divided into seven classes using multiple clusters, including three classes of flat kernels, three classes of round kernels and abnormal class. Further, the stepwise discriminant analysis was conducted to select...
Wireless multimedia sensor network (WMSN) has powerful multimedia signal acquisition and processing abilities. This paper proposes a collaborative hybrid classifier learning algorithm to achieve online support vector machine (SVM) learning for robust target classification in WMSN. The proposed algorithm is carried out in a hybrid computing paradigm, which combines the advantages of progressive computing...
A model based on means kernel clustering and support vector machine (SVM) multi-class object simplified structure is proposed for transformer fault diagnosis. The basic idea is, firstly, the training samples are clustered by means kernel clustering algorithm, then the right ones clustered by means kernel clustering are put into the classifier of SVM multi-class object simplified structure and trained...
Equable principal component analysis (EPCA) is a powerful technique of feature extracting. It can reduce a large set of correlated variables to a smaller number of uncorrelated components. Support vector machines (SVM) is a novel pattern classification approach. It is very efficient in solving clustering problems that are not linearly separable. This paper presents a method of expression recognition...
With the crucial problem of specifying cluster number in clustering algorithm, a cluster number specification-free algorithm, F-CMSVM, is proposed in this paper. Firstly, the data set is classified into two clusters by Fuzzy C-means algorithm (FCM). Then the result is tested by Support Vector Machine (SVM) associated with a fuzzy membership function to confirm whether the data set could be classified...
Security content filtering of World Wide Web is one of the important tasks among network security. The lower precision of Web mining based on keywords is a common fault, especially when those grouchy persons used active disturbing methods to cheat and bypass various filters. To filter these few but purposively or malicious Web pages the first thing is the classifier design. Therefore, a cascade mining...
Tikhonov regularized SVM is a kind of new SVM which can convert multi-class problems to be single optimized problems. Since SVM has some limitations in disposition of big data collection, this paper puts forward a new reduction Tikhonov regularized SVM by utilizing pruning algorithm to gain reduction data collection. Meanwhile, the paper applies genetic algorithm to make automatic selection from the...
The traditional data mining algorithms behaves undesirable in the instance of imbalanced data sets, as the distribution of the data sets is not taken into consideration when these algorithms are designed. This paper describes the Naive Bayes classification mechanism, and then points out that, random re-sampling methods not to be able to improve its performance. A negative-case-pruning Naive Bayes...
Personal credit scoring plays an important role for commercial banks to keep away from consumer credit risks. This paper used neural networks for personal credit scoring and used two evolutional algorithms of genetic algorithm (GA) and particle swarm optimization (PSO) to train the networks to construct a GA neural network and a PSO neural network respectively. The two neural networks were used to...
An variable precision rough set (RS) knowledge acquisition based on discrete particle swarm optimization (DPSO-VPRS) are proposed to solve rough set is lack of the ability of anti-jamming, which is used the information entropy is considered as a suitable function in discrete particle swarm algorithm and the attribute dependent degree of variable precision rough set is optimized, and make the classification...
To meet the robustness of the fault diagnosis algorithm for identifying the novel fault pattern, the method, which combines the supervised classification and unsupervised classification, is proposed in this paper. As the supervised classification, Learning vector quantity neural network is employed to classify sensor mode. As the unsupervised classification, subtractive clustering is applied to identify...
The correct identification of two-phase flow regime is the basis for the accuracy measurement of other flow parameters in two-phase flow measurement. Electrical capacitance tomography (ECT) is a new measurement technology. It is often used to identify two-phase/multi-phase flow regime and investigate the distribution of solids. The support vector machine (SVM) is a machine-learning algorithm based...
CRM, which aims to enhance the effectiveness and performance of the businesses by improving the customer satisfaction and loyalty, has now become a leading business strategy in highly competitive business environment. The objective of this research is to improve customer satisfaction on productpsilas colors with the aid of the expert system developed by the authors by using artificial neural networks...
Support vector machine (SVM) plays an important role in the data mining and knowledge discovery by constructing a non-linear optimal classifier. The key problem of training support vector machines is how to solve quadratic programming problem, which results in calculation difficulty while learning samples gets larger. The intelligent search techniques, such as genetic algorithm and particle swarm...
In order to improve the gait of disable people, a control concept of above-knee prosthesis was presented. The surface electromyography signal extracted from leg muscles was applied to recognize the phase of stride, and translated into on-off signal of self-lock control. A hybrid neural network-genetic algorithm was applied to describe the relation between surface electromyography signals and every...
Core vector machine (CVM) is an efficient kernel method for large data classification. It has prominent advantages in dealing with large data sets in high-dimensional space. This paper presents a novel geometric framework between CVM and the traditional support vector machine (SVM). We proved theoretically that: (1) In one-class classification, non-training examples on the surface of the exact minimum...
Data missing in data pretreatment has a serious effect on the accuracy of subsequent analysis results in data mining. In this paper, the cause of data missing and the corresponding effect on data mining were discussed. Then the missing pattern and the application limitation of traditional missing value estimation were analyzed as well. Finally, the estimation algorithm of missing value in MAR (missing...
Based on rough sets reducts, a new neural network ensemble method is proposed. Reducts with robustness and good generalization ability are achieved by a dynamic reduction technology. Then according to different reducts, multiple BP neural networks are designed as base classifiers. And with the idea of selective ensemble, the best neural network ensemble can be found by some search strategies. Finally,...
In recent years, back-propagation (BP) neural network has been widely applied to the remote sensing image classification. However, the BP method based on the gradient descent principle suffers from the problem of getting stuck at local minimum. In addition, only using spectral information for multispectral remote sensing image classification could not get the ideal result. In this paper, a new method...
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