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With the ever-improving degree of car privatization, the number of car-owning residents has repeatedly reached new highs, but it has also caused traffic congestion, especially lane congestion. To reduce emergency lane congestion, and realize the intelligent detection and classification of emergency lanes, this paper introduces logistic regression theory and proposes a vehicle detection method based on logistic regression. Based on the powerful feature abstraction and automatic learning capabilities of convolutional networks, it studies vehicles based on convolutional networks. The recognition algorithm analyses the contribution of the convolution kernel in the convolutional network, and transforms the original emergency lane vehicle detection. The research results show that the emergency lane vehicle detection rate based on logistic regression and a deep convolutional network designed in this paper can reach approximately 98%, which is higher than the detection methods based on SVM and AdaBoost, and the detection time is not different from SVM and AdaBoost. After training, the convolution kernel of the deep convolutional network, the detection time and detection accuracy improved to a certain extent. This shows that the emergency lane vehicle detection and classification method based on logistic regression and a deep convolutional network, can play an important role in emergency lane intelligence. [ABSTRACT FROM AUTHOR]

Different government media have different communication effects and users' emotional experience. It carries on a comparative research on government media selecting three different types of government media which include China's Police Online, Central Committee of the Communist Youth League, and China's Fire Control in the context of public health emergencies. Based on the deep learning technique, the emotion classification model of long-term memory network is constructed to analyze the emotion of the users' comments of different government media; taking the number of contents, the number of retweets, the number of praises, and the number of comments as evaluating indicators to do comparative analysis to cross platform government medias. Through the comparative results, it is found that different types and platforms of government media have great differences in users' emotional experience; the emotion performance of users' comments is strongly related to the information communication power and effectiveness of government media. [ABSTRACT FROM AUTHOR]

With the development of science and technology and the progress of the times, robots have slowly entered people's lives and work. However, how to control robots to bypass obstacles has become the focus of current research. Different from other related researches, the main research purpose of this paper is to use electroencephalogram (EEG) to control robots and achieve obstacles. In this paper, sample entropy is used to extract features of EEG, and then integrated learning is used to classify the extracted features. In the research process, this paper analyses the changes in the accuracy of the subjects before and after training, respectively, analyses the results of the two kinds of sports recognition, and then analyses the distribution of brain regions for motor control. Through the experiment on 12 subjects, the results show that through training, the four sports can achieve the highest recognition result of 85.4%. The experimental results show that, for the specific analysis of the distribution of brain regions, it can be seen that there are obvious differences in the characteristics of the left and right brains that control the movement of the robot, but there are differences in the control of the movement of the robot. [ABSTRACT FROM AUTHOR]

Computational complexity and sample selection are two main factors that limited the performance of online sequential extreme learning machines (OS-ELMs). This paper proposes a new model that introduces the concept of hybrid kernel and sample selection method based on an online learning model using a membership function. In other words, an online sequential extreme learning machine based on a hybrid kernel function (HKOS-ELM) is presented. The algorithm only calculates the kernel function to determine the final output function, mostly solving the computational complexity of the algorithm. The hybrid kernel function proposed in this paper has the advantages of strong learning ability and good generalization performance of single kernel function. Based on the classification essence of the OS-ELM classification, the membership function is introduced into the sample selection to remove the noise point and the outlier point. The experimental results showed that the HKOS-ELM algorithm adding the membership degree with mixed kernel functions preserves the advantages of kernel functions and online learning and improves the classification performance of the system. [ABSTRACT FROM AUTHOR]

Multiview learning problem refers to the problem of learning a classifier from multiple view data. In this data set, each data point is presented by multiple different views. In this paper, we propose a novel method for this problem. This method is based on two assumptions. The first assumption is that each data point has an intact feature vector, and each view is obtained by a linear transformation from the intact vector. The second assumption is that the intact vectors are discriminative, and in the intact space, we have a linear classifier to separate the positive class from the negative class. We define an intact vector for each data point, and a view-conditional transformation matrix for each view, and propose to reconstruct the multiple view feature vectors by the product of the corresponding intact vectors and transformation matrices. Moreover, we also propose a linear classifier in the intact space, and learn it jointly with the intact vectors. The learning problem is modeled by a minimization problem, and the objective function is composed of a Cauchy error estimator-based view-conditional reconstruction term over all data points and views, and a classification error term measured by hinge loss over all the intact vectors of all the data points. Some regularization terms are also imposed to different variables in the objective function. The minimization problem is solved by an iterative algorithm using alternate optimization strategy and gradient descent algorithm. The proposed algorithm shows its advantage in the compression to other multiview learning algorithms on benchmark data sets. [ABSTRACT FROM AUTHOR]