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Tea price evaluation is a key issue in the process of tea trade. The traditional tea price evaluating methods mainly depend on the experience of tea experts. The evaluating results using these methods are usually unstable and imprecise. So far, how to develop an automatic tea price evaluating system is still a challenging work. In this paper, we propose a data-mining-based tea price evaluation framework,...
Laplacian support vector machine could utilize the unlabeled samples for semi-supervised learning by applying the manifold regularization term. But the data adjacent graph in the manifold regularization term couldn't take advantage of the label information and the empirical setting of heat kernel parameter would also degrade the learning performance. Inspired by human behavioral learning theory, a...
With the arrival of population aging society, the health care of the elderly becomes more important. The fall detection algorithm is the core of the fall detection alarm system, so it is the key for the research and development of the fall detection system to analyze and select the appropriate algorithm for the detection of falling. It is one of the most important indicators of elder health monitor...
The feature selection, which can reduce the dimensionality of vector space without sacrificing the performance of the classifier, is widely used in text categorization. In this paper, we proposed a new feature selection algorithm, named CMFS, which comprehensively measures the significance of a term both in inter-category and intra-category. We evaluated CMFS on three benchmark document collections,...
Internet e-mails have become a common medium of communication for nearly every one. With the fast growing, spam interferes with valid email, and bothers users. This paper proposes a new fuzzy adaptive multi-population genetic algorithm (FAMGA), in order to automatically find the best feature subset to classify spam e-mails. FAMGA consists of multiple subpopulations, and each population runs independently...
Feature selection is a very important part for datamining, machinery learning and pattern recognition. Distance plays a vital role in Support Vector Machines (SVM) theory. Relief-F algorithm solves feature redundancy well but doesn't guarantee the maximum distance. To overcome this problem, a feature subset selection algorithm is proposed which takes SVM average distance as estimation rule and sequential...
The use of intelligent systems for stock market predictions has been widely established. This paper introduces a ensemble model of SVM and ANNs for the prediction of three stock indices. The performance of this model is then compared with support vector machine model and an artificial neural network respectively. Empirical results reveal that the ensemble result obtain the best results.
Superheater steam temperature in power plant is the strong nonlinearity system. Sparse Least squares support vector networks (LSSVN) are proposed to model the superheated steam of power plant in this paper. The structure is obtained by equality constrained minimization. By combining the DMC with discount recursive partial least squares (DRPLS), a adaptive DMC control method based on discount recursive...
Super-heater steam temperature in power plant is the strong nonlinearity system. Though neural networks have the ability to approximate nonlinear functions with arbitrary accuracy, good generalization results are obtained only if the structure of the network is suitably chosen. Therefore, selecting the "best" structure of the neural network is more difficulty. Sparse least squares support...
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