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MapReduce is a popular programming model widely used in distributed systems. With regard to large-scale applications, e.g. home energy management in a city, online social community etc., load-balancing becomes critical affecting the performance of distributed computing. Present proposed load-balancing approaches in MapReduce aim at optimizing task execution time, whereas disk space is not considered...
In many applications, it is very expensive or time consuming to obtain a lot of labeled examples. One practically important problem is: can the labeled data from other related sources help predict the target task, even if they have 1) different feature spaces (e.g., image versus text data), 2) different data distributions, and 3) different output spaces? This paper proposes a solution and discusses...
Sequence data plays an important role in data analysis applications, such as sequence classification. One important aspect of sequence data analysis is to obtain the labeled sequence data and use a machine learning model to predict the sequence structures. Conditional Random Fields (CRF) is such a machine learning method which is popular used in sequential data analysis. This is because that CRF can...
To achieve the analysis of characteristic and forecasting of the mobile communication traffic, a mobile communication traffic modeling and forecasting method by Least Squares Support Vector Machine(LS-SVM) is proposed. With this method, an on-line forecasting scheme is designed to realize short-time forecasting of the mobile communication traffic. The traffic data is provided by China Mobile Communications...
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