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The main chemical compositions of tobacco is one of the significant factors to decide the quality of tobacco, and it is time consuming, expensive, unrepeatable and destructive to obtain the chemical compositions in laboratories. This paper investigates the relationship between tobacco near infrared (NIR) spectra and chemical composition, thus the chemical composition can be easily obtained through...
Traffic flow prediction has become a hot spot in the intelligent transportation system study. In this paper, novel methods are proposed to predict traffic flow. We divide 24 hours into 4 stages according to the bimodal distribution of traffic flow, and integrate topology features of urban traffic network into 4 typical machine learning methods. Experiments on the traffic flow of Qinhuangdao city demonstrate...
Many elderly prefer to live independently at their own homes. However, how to use modern technologies to ensure their safety presents vast challenges and opportunities. Being able to non-intrusively sense the activities performed by the elderly definitely has great advantages in various circumstances. Non-intrusive activity recognition can be performed using the embedded sensors in modern smartphones...
Social media has become an important source of near-instantaneous information about events and is increasingly also being analysed to provide predictive models, sentiment analysis and so on. One domain where social media data has value is transport and this paper looks at the exploitation of Twitter data in traffic management. A key issue is the identification and analysis of traffic-relevant content...
The support vector machine (SVM) method, as a promising classification technique, has been widely used in various fields due to its high efficiency. However, SVM cannot effectively solve online classification problems since, when a new sample is misclassified, the classifier has to be retrained with all training samples plus the new sample, which is time consuming. According to the geometric characteristics...
Multi-scale kernel function learning is a special case of multi-kernel learning, namely combines several multi-scale kernels. This approach is more flexible. It provides more comprehensive choice of scale than the mixed kernel learning. In this paper, the model's parameters of multi-scale Gaussian kernel were used as elementary particles. The parameters of multi-scale Gaussian kernel were global optimized...
Twin SVM (TWSVM), as a computationally effective classification tool, is shown to be better than GEPSVM and SVM in favor of classification effectiveness. However, two dual QPPs arising from TWSVM leads to the higher computational time compared to GEPSVM and one has to look for approximate solutions when the data points are very large. In this paper, by slightly reformulating the primal problem of...
Traditionally, multi-plane Support Vector Machines including twin support vector machine (TWSVM) and least squares twin support vector machine (LSTSVM) essentially fail to fully discover the local geometry inside the samples that may be important for classification performance and only preserve the global data structure. This motivates the rush towards new classifiers that can take advantage of underlying...
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