基于SVM的汉语评论情感分类方法研究

情感分类是一项具有较大实用价值的分类技术．它可以对网上纷繁复杂的信息进行情感倾向标注．为用户提供一个简洁的总结信息，进而为人们制定决策提供帮助，然而目前针对汉语的情感分类开展的工作并不多。提出一种基于SVM机器学习的情感分类方法，并引入基于2-POS模型的句子主观性分析方法，利用SVM进行机器学习，实现汉语评论的情感分类。实验表明这种方法能够有效地判定评论信息的情感倾向。     

Reproducibility of Artificial Cut on Heel Area of Rubber Outsole

Randomly acquired characteristics (RAC) are seldom used for exclusion in footwear examinations because they can disappear owing to wear. To help examiners explain discrepancies in RAC, this study investigated the reproducibility of cuts. One cut was made on the heel area of each shoe outsole and then measured. Changes in cuts were statistically evaluated, correlations between their variations and the weight/height of the subject were assessed, and the Support Vector Machine was used for the first time to study their discrimination probability. Most cuts became larger at first and then became smaller. The solidities of most cuts increased after 21 days, and the variation in the cut and the subject's weight/height were negatively correlated. Although the discrimination probability declined as the cut aged, 77% of the same-source and 96.88% of the different-source cuts could be identified correctly after 6 months, indicating that cuts on the heel area are relatively reliable.     

Digital camera identification using PRNU: A feature based approach

Source camera identification is one of the emerging field in digital image forensics, which aims at identifying the source camera used for capturing the given image. The technique uses photo response non-uniformity (PRNU) noise as a camera fingerprint, as it is found to be one of the unique characteristic which is capable of distinguishing the images even if they are captured from similar cameras. Most of the existing PRNU based approaches are very sensitive to the random noise components existing in the estimated PRNU, and also they are not robust when some simple manipulations are performed on the images. Hence a new feature based approach of PRNU is proposed for the source camera identification by choosing the features which are robust for image manipulations. The PRNU noise is extracted from the images using wavelet based denoising method and is represented by higher order wavelet statistics (HOWS), which are invariant features for image manipulations and geometric variations. The features are fed to support vector machine classifiers to identify the originating source camera for the given image and the results have been verified by performing ten-fold cross validation technique. The experiments have been carried out using the images captured from various cell phone cameras and it demonstrated that the proposed algorithm is capable of identifying the source camera of the given image with good accuracy. The developed technique can be used for differentiating the images, even if they are captured from similar cameras, which belongs to same make and model. The analysis have also showed that the proposed technique remains robust even if the images are subjected to simple manipulations or geometric variations.     

PCA和支持向量机在财务分析中的应用

提出了基于主成分分析（PCA）的支持向量机分类原理，并应用于财务指标的分析研究。主要思想是先将训练样本和测试样本的属性集进行标准化处理，计算各属性间的相关程度，构造新属性集，然后使用支持向量分类机对训练样本进行训练，构造决策函数，最后将决策函数对测试样本进行测试。本文选择我国2000年末106家上市公司的主要财务指标进行经营状况的分类测试，实验结果表明，使用本文的分类模型可提高模式识别的准确度。