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国家自然科学基金(61100231)

作品数:3 被引量:2H指数:1
相关作者:侯红霞赵庆兰张雪锋董晓丽更多>>
相关机构:西安邮电大学更多>>
发文基金:国家自然科学基金陕西省教育厅科研计划项目更多>>
相关领域:电子电信自动化与计算机技术更多>>

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Rotated hyperbola model for smooth support vector machine for classification
2018年
This article puts forward a novel smooth rotated hyperbola model for support vector machine( RHSSVM) for classification. As is well known,the support vector machine( SVM) is based on statistical learning theory( SLT)and performs its high precision on data classification. However,the objective function is non-differentiable at the zero point. Therefore the fast algorithms cannot be used to train and test the SVM. To deal with it,the proposed method is based on the approximation property of the hyperbola to its asymptotic lines. Firstly,we describe the development of RHSSVM from the basic linear SVM optimization programming. Then we extend the linear model to non-linear model. We prove the solution of RHSSVM is convergent,unique,and global optimal. We show how RHSSVM can be practically implemented. At last,the theoretical analysis illustrates that compared with other three typical models,the rotated hyperbola model has the least error on approximating the plus function. Meanwhile,computer simulations show that the RHSSVM can reduce the consuming time at most 54. 6% and can efficiently handle large scale and high dimensional programming.
Wang En
关键词:CLASSIFICATIONSVM
Smooth support vector machine based on piecewise function被引量:2
2013年
Support vector machines (SVMs) have shown remarkable success in many applications. However, the non-smooth feature of objective function is a limitation in practical application of SVMs. To overcome this disadvantage, a twice continuously differentiable piecewise-smooth function is constructed to smooth the objective function of unconstrained support vector machine (SVM), and it issues a piecewise-smooth support vector machine (PWESSVM). Comparing to the other smooth approximation functions, the smooth precision has an obvious improvement. The theoretical analysis shows PWESSVM is globally convergent. Numerical results and comparisons demonstrate the classification performance of our algorithm is better than other competitive baselines.
WU QingFAN Jiu-lun
关键词:SVM
8~10轮AES-128的biclique攻击
2013年
高级加密标准(AES)是信息安全中实现数据加密、认证和密钥管理的核心分组密码算法,其安全性分析是密码学的重要课题之一.本文利用AES独立的相关密钥差分,构造了3轮biclique;基于该biclique,使用重计算技术,针对8~10轮AES-128给出新攻击.研究结果表明,攻击8轮AES-128所需的数据复杂度为264选择密文,时间复杂度为2125.29次加密;攻击9轮AES-128所需的数据复杂度为264选择密文,时间复杂度为2125.80次加密;攻击10轮AES-128所需的数据复杂度为264选择密文,时间复杂度为2126.25次加密.与已有的同轮攻击结果相比,新分析所需要的时间复杂度或数据复杂度降低.
董晓丽张雪锋侯红霞赵庆兰
关键词:密码分析分组密码
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