OBJECTIVE: With Sina Weibo data as the background, support vector machine (SVM) and k-nearest neighbor (KNN) method are used to predict and analyze the user’s micro-blog emotion and related behavior in social network, hoping to obtain rich potential business value. METHODS: First, the API interface of Sina Weibo is utilized to obtain the information of users in Sina Weibo; then, the Excel software is utilized to sort and analyze the extracted data to extract the features of micro- blogs posted by users. Second, SVM and KNN algorithms are utilized to calculate the weighted average and propose a hybrid multi-classifier-based Mixed Classifier Emotion Prediction Model (MCEPM). Finally, through the evaluation criteria, including precision (P), recall rate (R), and harmonic average (F1), the specific experimental results of SVM and KNN weight coefficients are compared with the prediction results of MCEPM. RESULTS: The prediction effect of MCEPM is associated with the weight coefficients of SVM and KNN. If the weight coefficients of SVM and KNN are 0.6 and 0.4, the prediction effect of MCEPM will be optimal. Comprehensive analysis shows that the MCEPM model can balance the prediction results of the positive and negative samples of the two classifiers. CONCLUSION: MCEPM model is superior to other algorithms in micro-blog emotion prediction, which can help enterprises analyze users’ product inclination and provide accurate customer service requirements for enterprises.
Real-time gesture detection and tracking algorithm is proposed to solve the problems of detection and tracking of gesture under the complex background. Firstly, an Adaboost cascade classifier is used to track by the feature model and classifier, which are constructed by real-time compressive tracking algorithm. The negative factors from the gesture posture, shade, skin color etc. are eliminated to improve the performance of the gesture detection and tracking by fusing the responses of classifier of real-time compressive tracking algorithm and the results of gesture detection based on Adaboost algorithms. Paper proposed algorithms can self-recovery when the tracking object is missed, so continuous recognition and tracking is guaranteed. Finally, several experiments are given to verify the developed algorithm and to demonstrate its practicality and effectiveness.
Under the study of Kernel Methods, this paper put forward two improved algorithm which called R-SVM & I-SVDD in order to cope with the imbalanced data sets in closed systems. R-SVM used K-means algorithm clustering space samples while I-SVDD improved the performance of original SVDD by imbalanced sample training. Experiment of two sets of system call data set shows that these two algorithms are more effectively and R-SVM has a lower complexity.
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