Adaptive kernel scaling support vector machine with application to a prostate cancer image study

The support vector machine (SVM) is a popularly used classifier in applications such as pattern recognition, texture mining and image retrieval owing to its flexibility and interpretability. However, its performance deteriorates when the response classes are imbalanced. To enhance the performance of the support vector machine classifier in the imbalanced cases we investigate a new two stage method by adaptively scaling the kernel function. Based on the information obtained from the standard SVM in the first stage, we conformally rescale the kernel function in a data adaptive fashion in the second stage so that the separation between two classes can be effectively enlarged with incorporation of observation imbalance. The proposed method takes into account the location of the support vectors in the feature space, therefore is especially appealing when the response classes are imbalanced. The resulting algorithm can efficiently improve the classification accuracy, which is confirmed by intensive numerical studies as well as a real prostate cancer imaging data application.     

机器翻译中多义词的消歧

鉴于自然语言语序的灵活性,使得自然语言知识的自动消歧很困难。提出一种机器翻译中多义词词义消歧的新方法,从语境相关素的角度着手,把人类消歧时进行的逻辑推理归结为一种便于机器操作的机械的集合运算,提高了消歧的准确率。     

"机械设计基础"实验教学改革探讨

文章结合"机械设计基础"课程在机械类专业教学中的地位及课程的任务,对"机械设计基础"实验教学的教学内容、教学方法及教学手段改革进行了初步的探讨。     

Data Mining and Knowledge Discovery in Databases – An Overview

Data mining seeks to extract useful, but previously unknown, information from typically massive collections of non-experimental, sometimes non-traditional data. From the perspective of statisticians, this paper surveys techniques used and contributions from fields such as data warehousing, machine learning from artificial intelligence, and visualization as well as statistics. It concludes that statistical thinking and design of analysis, as exemplified by achievements in clinical epidemiology, may fit well with the emerging activities of data mining and 'knowledge discovery in databases' (DM&KDD).     

多类支持向量机的DDoS攻击检测的方法

为了利用SVM准确的检测DDoS,需要找到区分正常流和攻击流的特征向量,根据DDoS攻击的特点,提出了独立于流量的相对值特征向量。为了在指示攻击存在的同时,也指示攻击强度,多类支持向量机(MCSVM)被引入到DDoS检测中。实验表明,RLT特征与MCSVM相结合,可以有效检测到不同类型的DDoS攻击,并且能准确地指示攻击强度,优于目前已有的检测方法。使用RLT特征进行DDoS检测,比使用单一攻击特征进行识别的方法,包含更多的攻击信息,可以得到较高的检测精度。     

综合评价法的统一化研究

从函数分析入手,将综合评价方法归结为标准化函数、基函数以及权数三者的选择和确定,并将综合评价法与统计回归法以及机器学习法统一起来,从而在较高层面上看待不断演化出来的综合评价方法,有利于综合评价方法的理论统一化。     

有限自动机在BBS信息监测系统中的运用

形式语言与自动机理论是为了将自然语言转换成为计算机能够识别、处理的语言而建立的理论体系,利用有限自动机可以对文本信息进行智能化监测,对文本的词法分析可以得到系统监测所需要的信息。该文对有限自动机在BBS信息监测系统中的运用进行了详细阐述;描述了系统的具体实现以及软件界面;对信息监测的三种方式终端监测、运行监测、备份监测的优劣进行了分析,并提出了具体实施的办法。     

Preemptive Machine Covering on Parallel Machines

This paper investigates the preemptive parallel machine scheduling to maximize the minimum machine completion time. We first show the off-line version can be solved in O(mn) time for general m-uniform-machine case. Then we study the on-line version. We show that any randomized on-line algorithm must have a competitive ratio m for m-uniform-machine case and ∑_{i = 1}^m1/i for m-identical-machine case. Lastly, we focus on two-uniform-machine case. We present an on-line deterministic algorithm whose competitive ratio matches the lower bound of the on-line problem for every machine speed ratio s≥ 1. We further consider the case that idle time is allowed to be introduced in the procedure of assigning jobs and the objective becomes to maximize the continuous period of time (starting from time zero) when both machines are busy. We present an on-line deterministic algorithm whose competitive ratio matches the lower bound of the problem for every s≥ 1. We show that randomization does not help.     

机构的速比对机械效率的影响

根据斜面及杠杆这两种基本的变速方式,讨论了机构的机械效率与其速比之间的关系,得出了采用杠杆方式变速较斜面方式变速可以获得较大的机械效率的结论.     

材料力学实验教学改革的探索与实践

抓住机遇 ,开拓创新 ,研制开发一些功能强大、性能价格比高的实验教学设备 ,开设创新性实验 ,加强学生的创新意识 ,拓展学生视野 ,提高实验教学效果 ,加深材料力学实验教学改革。