针对目标在形状、外观和光照条件发生较大变化时产生的检测率低的问题,以牛体检测为例提出了基于Gentle AdaBoost算法的牛体检测。利用bag of features(BOF)的思想创建特征词典,然后通过词典对牛体目标进行特征提取,最后通过Gentle AdaB...针对目标在形状、外观和光照条件发生较大变化时产生的检测率低的问题,以牛体检测为例提出了基于Gentle AdaBoost算法的牛体检测。利用bag of features(BOF)的思想创建特征词典,然后通过词典对牛体目标进行特征提取,最后通过Gentle AdaBoost算法对训练集的BOF特征向量进行训练分类,获得目标对象和场景的分类模型。实验结果表明,该算法训练的检测器在牛体目标存在光照不均匀、形变时均可实现可靠的检测。展开更多
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 hig...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.展开更多
文摘针对目标在形状、外观和光照条件发生较大变化时产生的检测率低的问题,以牛体检测为例提出了基于Gentle AdaBoost算法的牛体检测。利用bag of features(BOF)的思想创建特征词典,然后通过词典对牛体目标进行特征提取,最后通过Gentle AdaBoost算法对训练集的BOF特征向量进行训练分类,获得目标对象和场景的分类模型。实验结果表明,该算法训练的检测器在牛体目标存在光照不均匀、形变时均可实现可靠的检测。
基金Projects(61170156,60933005)supported by the National Natural Science Foundation of China
文摘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.
