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The deep learning based trackers can always achieve high tracking precision and strong adaptability in different scenarios. However, due to the fact that the number of the parameter is large and the fine-tuning is challenging, the time complexity is high. In order to improve the efficiency, we proposed a tracker based on fast deep learning through constructing a new network with less redundancy. Based...
In order to improve the accuracy of remote sensing image classification, this paper firstly improves the kernel function of support vector machines (SVMs), after which the Markov Random Field (MRF) stochastic model is combined with the SVM model to classify the images. The remote sensing experimental area in northwest Indiana is shot in June 1992. A VIRIS hyperspectral remote sensing images are used...
Support Vector Machine (SVM) and Logistic Regression (LR) are two popular classification models. The main purpose of a classification algorithm is to figure out the estimator for the decision boundary. In this paper, we considered confidence bands of decision boundary generated from SVM and LR. Confidence bands of decision boundary are estimated through bootstrap methods. We compared the confidence...
Based on analysis of the process of rotary dryer kiln, a soft- sensor model for water content of the dregs by using the support vector machines (SVM) is proposed. The parameters of SVM are optimized through the hybrid optimization algorithm which combines the genetic search with the local search, first the kernel function and SVM parameters are optimized roughly through genetic algorithm, after certain...
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