计算机引导型病毒的防治初探

本文着重介绍计算机引导型病毒,从病毒的工作原理出发,介绍了病毒的几种检测、消除及免疫的方法.     

晶体常数α、β、γ与α^＊、β^＊、γ^＊的几何关系

研究晶体常数α、β、γ与α^*，β^*，γ^*之间的几何关系，给出了它们在球面上的表示。     

基于支持向量机的siRNA降解效率预测

为了辅助siRNA的设计,从已发表文献中共收集到573个siRNA的实验数据,使用基于统计学习理论的支持向量机(SVM)方法,提取了siRNA序列的碱基对关联性(BBC)特征,然后使用十倍交叉验证方法,对siRNA沉默目标基因的效率进行了预测.结果表明,基于支持向量机,选用多项式核作为核函数的算法具有最高的AUC值(0.73,ROC曲线图)和最高的r值(0.43,Pearson相关系数分析),优于以前基于打分的算法.结果说明,在以后的siRNA的设计中应该更多关注碱基之间的关联信息.     

线性方程组和矩阵的中数式教学提纲

本文以中学数学的陈述方式引入《高等代数》课程的线性方程组和矩阵的重要概念和相关的运算法则,从实例出发将抽象的定义、法则具体化和通俗化。     

关于极矢量与轴矢量的探讨

论述了极矢量与轴矢量一个重要方面的差异，介绍极矢量与轴矢量的判别方法，证明极矢量与轴矢量的一些矢量乘法规则．     

Classifying Data Sets Using Support Vector Machines Based on Geometric Distance

Support vector machines (SVMs) are not as favored for large-scale data mining as for pattern recognition and machine learning because the training complexity of SVMs is highly dependent on the size of data set. This paper presents a geometric distance-based SVM (GDB-SVM). It takes the distance between a point and classified hyperplane as classification rule, and is designed on the basis of theoretical analysis and geometric intuition. Experimental code is derived from LibSVM with Microsoft Visual C 6.0 as system of translating and editing. Four predicted results of five of GDB-SVM are better than those of the method of one against all (OAA). Three predicted results of five of GDB-SVM are better than those of the method of one against one (OAO). Experiments on real data sets show that GDB-SVM is not only superior to the methods of OAA and OAO, but highly scalable for large data sets while generating high classification accuracy.     

酉空间中三种线性变换之间的关系

酉空间中两种重要的线性变换；酉变换和Hermite变换，本文给出另一种线性变换-对合变换，以及三种线性变换之间的关系。     

矩阵方程AX=XB的HERMITE解

本文给出了矩阵方程有Hermite解,Hermite半正定解和Hermite正定解的必要且充分条件,并给出了解的表达式。     

首次积分与极限集的关系

利用奇点和有限集的特征，给出了首次积分存在的某些条件，首次积分可用来判别奇点，在各种奇点分布的情况下，利用向量场同胚映射证明了非奇点极限集的存在性。     

Probability output of multi-class support vector machines

A novel approach to interpret the outputs of multi-class support vector machines is proposed in this paper. Using the geometrical interpretation of the classifying heperplane and the distance of the pattern from the hyperplane, one can calculate the posterior probability in binary classification case. This paper focuses on the probability output in multi-class phase where both the one-against-one and one-against-rest strategies are considered. Experiment on the speaker verification showed that this method has high performance.