广义后缀树及其在汉语科技词系统中的应用研究

科技词汇知识是科技信息智能处理的基石,如何加速汉语科技词系统的构建是目前研究的热点问题之·。考虑到中文术语构词的特点,文章引入了一种灵活的数据结构——广义后缀树,从字面的角度提出了关系辅助构建、任务分配以及输入提示等辅助工具,使得知识工程师的工作更加高效。     

基于特征矢量稀疏分解的非圆信号DOA估计

文中提出当信源为非圆信号时,基于特征矢量稀疏分解进行DOA估计;并在稀疏恢复过程中,比较空间范数变化对误差的影响.该方法对协方差矩阵进行了扩展,在利用L曲线方法自适应得到正则化参数的同时,对空间范数应用进行了推广.不仅提高信息利用率,能够处理相干信号源,而且不需要已知信号源数目,性能优于平滑处理过后的NC-MUSIC算法.     

Self-training from labeled features for sentiment analysis

Sentiment analysis concerns about automatically identifying sentiment or opinion expressed in a given piece of text. Most prior work either use prior lexical knowledge defined as sentiment polarity of words or view the task as a text classification problem and rely on labeled corpora to train a sentiment classifier. While lexicon-based approaches do not adapt well to different domains, corpus-based approaches require expensive manual annotation effort.     

A generalized cluster centroid based classifier for text categorization

In this paper, a Generalized Cluster Centroid based Classifier (GCCC) and its variants for text categorization are proposed by utilizing a clustering algorithm to integrate two well-known classifiers, i.e., the K-nearest-neighbor (KNN) classifier and the Rocchio classifier. KNN, a lazy learning method, suffers from inefficiency in online categorization while achieving remarkable effectiveness. Rocchio, which has efficient categorization performance, fails to obtain an expressive categorization model due to its inherent linear separability assumption. Our proposed method mainly focuses on two points: one point is that we use a clustering algorithm to strengthen the expressiveness of the Rocchio model; another one is that we employ the improved Rocchio model to speed up the categorization process of KNN. Extensive experiments conducted on both English and Chinese corpora show that GCCC and its variants have better categorization ability than some state-of-the-art classifiers, i.e., Rocchio, KNN and Support Vector Machine (SVM).     

关于“大文件”概念的辨析

本文对大文件概念提出的缘由、内涵及与其相关的广义文件概念、档案概念之间的关系等作了交流探讨。     

Average SNR of maximum ratio transmission with selection combining

Two kinds of selection combining schemes including generalized selection combining （GSC） and generalized order selection combining （GOSC） are investigated. In the GSC scheme, L strongest diversity branches from a total of R diversity branches are selected and coherently combined by maximal ratio combining. GOSC means that the Lth strongest diversity branch from R diversity branches is selected for reception. Closed-form expressions for the average signal-to-noise ratios of maximum ratio transmission with GSC and GOSC are derived in Rayleigh fading channels.     

Polar coordinates and generalized h-type indices

This article highlights the importance of using polar coordinates when studying functions, in particular in relation to generalized h-type indices. Concretely, generalized h-type indices are essentially polar coordinates. This observation ties informetric ideas to standard mathematics. This article is essentially meant to provide tools for further studies.     

广义论证的符号学解读

广义论证扩大了逻辑家族成员,使之包括现代文化之外其他文化的逻辑,拓展逻辑学研究的空间。同时,广义论证的特征与符号学的相关内容具相同性或相近性,从中彰显出逻辑学与符号学的紧密联系。     

GSOR最优松弛因子选取方法的研究

对线性方程组数值解法中的广义逐次超松弛迭代法（GSOR）进行了算法分析,对广义逐次超松弛迭代法中最优松弛因子Ω的选取方法提出了不同的两种方法,并对两种方法进行了分析和程序设计,通过实例验证了两种方法可行性和有效性。     

The solution to matrix equation AX+XC=B

In this note, the problem of solution to the matrix equation AX+X^TC=B is considered by the Moore-Penrose generalized inverse matrix. A general solution to this equation is obtained. At the same time, some useful conclusions are made, which play important roles in the linear system theories and applications.