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Constructing accurate models that represent the underlying structure of Big Data is a costly process that usually constitutes a compromise between computation time and model accuracy. Methods addressing these issues often employ parallelisation to handle processing. Many of these methods target the Support Vector Machine (SVM) and provide a significant speed up over batch approaches. However, the...
With the appearance of large-scale database and people's increasing concern about individual privacy, privacy-preserving data mining becomes a hot study area, to which the support vector machine(SVM) belongs. In this paper, a novel privacy-preserving SVM for horizontally partitioned data is given. It has comparable accuracy to that of an ordinary SVM as we obtain the SVM by using the distinct property...
Accurate rainfall forecasting has been one of the most important role in order to reduce the risk to life and to alleviate economic losses by natural disasters. Recently, support vector regression (SVR) provides an alternative approach for developing rainfall forecasting model due to the use of a risk function consisting of the empirical error and a regularized term which is derived from the structural...
City scientific and technological progress level classification and promotion play a central role in spurring city income growth and reducing poverty. Based on the Chinese city data availability, this paper built evaluation index system on the level of city scientific and technological progress. According to the city scientific and technological progress data which is large scale and imbalance, this...
Innovation system efficiency analysis and prediction play an important role in regional innovation systems development and improve benefit of innovative capacity for country. According to the county innovation system data which is large scale and imbalance, this paper presented a support vector machine model to predict county innovation system efficiency. The method was compared with artificial neural...
Talents are the most important resource of high-tech enterprises. Thus conducting an effective early-warning of the brain drain in high-tech enterprises, will effectively reduce the brain drain acts to reduce the loss of high-tech enterprises. This paper, using of high-tech enterprise day-to-day performance appraisal data, in accordance with the characteristics of Chinese high-tech enterprises, carry...
It is very important to construct the training set and determine the sample number in the regression problem. In this paper, a new idea of constructing the training set is elaborated. The key point of this idea is to choose the hyper-parameters before determining the training set. More importantly, a heuristic approach is proposed to select samples of support vector machine (SVM). Using these methods,...
Machine learning algorithms for large scale data are becoming more crucial in today's world. This is due to the unprecedented size of streaming data being collected by information technology. Incremental learning is considered one of the key concepts for learning from streaming data where a learned model is updated when new data becomes available in time. In this paper, we study RBF-SVM local incremental...
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed general approach to formalizing such problems, known as learning...
Outlier detection in wireless sensor networks is essential to ensure data quality, secure monitoring and reliable detection of interesting and critical events. A key challenge for outlier detection in wireless sensor networks is to adaptively identify outliers in an online manner with a high accuracy while maintaining the resource consumption of the network to a minimum. In this paper, we propose...
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