融合改进RF算法的人体姿态识别方法在运动训练领域的应用

对人体姿态识别及现代智能化工程设计成为人机交互领域的重要研究方向进行了研究;在实现更高效、智能的人体姿态识别中,采用了基于DBSCAN-RF算法的分类训练器,同时对RF算法加以改进,引入了HD-SMOTE方法;该方法的技术创新和独特之处在于结合了密度聚类和随机森林的优点,能够有效地处理带有噪声的数据集,并具有较高的计算效率和可扩展性;通过实验测试,DBSCAN-RF算法的识别召回率最高达到了98.64%,相比于传统的RF算法、K-means-RF以及Mean-shift-RF算法,其数值分别增加了6.37%、4.28%、3.95%;同时,DBSCAN-RF算法在跌倒和正常走路的识别召回率分别达到了95.31%和96.48%;此外,DBSCAN-RF算法的测试时间均低于62 ms;经实际应用满足了现代智能化的人体姿态识别工程上的应用,为现代智能化的人体姿态识别提供了可靠的技术支持。

Application of Artificial Neural Network to Battlefield Target Classification

To study the capacity of artificial neural network (ANN) applying to battlefield target classification and result of classification, according to the characteristics of battlefield target acoustic and seismic signals, an on the spot experiment was carried out to derive acoustic and seismic signals of a tank and jeep by special experiment system. Experiment data processed by fast Fourier transform(FFT) were used to train the ANN to distinguish the two battlefield targets. The ANN classifier was performed by the special program based on the modified back propagation (BP) algorithm. The ANN classifier has high correct identification rates for acoustic and seismic signals of battlefield targets, and is suitable for the classification of battlefield targets. The modified BP algorithm eliminates oscillations and local minimum of the standard BP algorithm, and enhances the convergence rate of the ANN.

Construction of unsupervised sentiment classifier on idioms resources

Sentiment analysis is the computational study of how opinions, attitudes, emotions, and perspectives are expressed in language, and has been the important task of natural language processing. Sentiment analysis is highly valuable for both research and practical applications. The focuses were put on the difficulties in the construction of sentiment classifiers which normally need tremendous labeled domain training data, and a novel unsupervised framework was proposed to make use of the Chinese idiom resources to develop a general sentiment classifier. Furthermore, the domain adaption of general sentiment classifier was improved by taking the general classifier as the base of a self-training procedure to get a domain self-training sentiment classifier. To validate the effect of the unsupervised framework, several experiments were carried out on publicly available Chinese online reviews dataset. The experiments show that the proposed framework is effective and achieves encouraging results. Specifically, the general classifier outperforms two baselines(a Na?ve 50% baseline and a cross-domain classifier), and the bootstrapping self-training classifier approximates the upper bound domain-specific classifier with the lowest accuracy of 81.5%, but the performance is more stable and the framework needs no labeled training dataset.

An Accurate and Extensible Machine Learning Classifier for Flow-Level Traffic Classification

Machine Learning(ML) techniques have been widely applied in recent traffic classification.However, the problems of both discriminator bias and class imbalance decrease the accuracies of ML based traffic classifier. In this paper, we propose an accurate and extensible traffic classifier. Specifically, to address the discriminator bias issue, our classifier is built by making an optimal cascade of binary sub-classifiers, where each binary sub-classifier is trained independently with the discriminators used for identifying application specific traffic. Moreover, to balance a training dataset,we apply SMOTE algorithm in generating artificial training samples for minority classes.We evaluate our classifier on two datasets collected from different network border routers.Compared with the previous multi-class traffic classifiers built in one-time training process,our classifier achieves much higher F-Measure and AUC for each application.

Words alignment based on association rules for cross-domain sentiment classification

Automatic classification of sentiment data（e.g., reviews, blogs） has many applications in enterprise user management systems, and can help us understand people＇s attitudes about products or services. However, it is difficult to train an accurate sentiment classifier for different domains. One of the major reasons is that people often use different words to express the same sentiment in different domains, and we cannot easily find a direct mapping relationship between them to reduce the differences between domains. So, the accuracy of the sentiment classifier will decline sharply when we apply a classifier trained in one domain to other domains. In this paper, we propose a novel approach called words alignment based on association rules（WAAR） for cross-domain sentiment classification,which can establish an indirect mapping relationship between domain-specific words in different domains by learning the strong association rules between domain-shared words and domain-specific words in the same domain. In this way, the differences between the source domain and target domain can be reduced to some extent, and a more accurate cross-domain classifier can be trained. Experimental results on Amazon~ datasets show the effectiveness of our approach on improving the performance of cross-domain sentiment classification.