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2011 10th IEEE/ACIS International Conference on Computer and Information Science最新文献

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An Improvement in Lossless Data Compression via Substring Enumeration 基于子串枚举的无损数据压缩改进
K. Iwata, M. Arimura, Yuki Shima
Dube ´ and Beaudoin proposed a new technique of loss less data compression called compression via sub string enumeration (CSE) in 2010. It has been indicated that the compression ratio of CSE achieves competitive performance for ones of the best PPM variants and BZIP2 from the viewpoint of experimental results. We refine the technique of CSE to reduce the candidate value of range to encode, and make the compression performance of our improvement clear analytically for some input strings, which have zero entropy rate. We show that the performance of compression ratio of the improved CSE never becomes worse than one of the original CSE for any source string in linear-time and linear-space complexity for the length of string.
Dube和Beaudoin在2010年提出了一种新的无损数据压缩技术,称为通过子串枚举(CSE)压缩。从实验结果来看,CSE的压缩比与最佳PPM变体之一和BZIP2具有竞争性能。我们对CSE技术进行了改进,减少了需要编码的范围的候选值,并对一些熵率为零的输入字符串的压缩性能进行了分析。结果表明,对于任意源字符串,改进CSE的压缩比性能在字符串长度的线性时间和线性空间复杂度上都不会比原始CSE差。
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引用次数: 8
Evaluation of Social Stability Based on Multi-class Support Vector Machine 基于多类支持向量机的社会稳定性评价
Qianqian Li, Yijun Liu, Wenyuan Niu
In the relationship of "Reform, Development, Stability", social stability is the foundation of making a nation work well. Therefore, this paper gets into the in-depth source on why the society shows unstable sometimes and establishes an index system for evaluating the social stability trend from the perspective of social combustion theory which is the kernel component of social physics. Furthermore, we construct a mathematical model to assess the social stability by applying multi-class support vector machine. By result analysis, the prediction of multi-class support vector machine is much identical to the reality, which is significant to construct a harmony society.
在“改革、发展、稳定”的关系中,社会稳定是一个国家运转良好的基础。因此,本文从社会物理学的核心组成部分——社会燃烧理论的视角出发,深入探究社会有时出现不稳定现象的根源,建立评价社会稳定趋势的指标体系。在此基础上,利用多类支持向量机构建了社会稳定性评价的数学模型。结果分析表明,多类支持向量机的预测结果与实际情况基本吻合,对构建和谐社会具有重要意义。
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引用次数: 0
Predicting of Oxidoreductase and Lyase Subclasses by Using Support Vector Machine 用支持向量机预测氧化还原酶和裂解酶亚类
Y. Wang, Xiuzhen Hu
Based on enzyme sequence, using composite vector with amino acid composition, low frequency of power spectral density, predicted secondary structure, value of autocorrelation function and motif frequency to express the information of sequence, an approach of support vector machine (SVM) for predicting 18 subclasses of oxidoreductases and 6 subclasses of lyases is proposed. By the Jackknife test, the overall success rates are 89. 9% and 95.1%, our predictive results are better than pervious results Keywords-enzyme, ¦Â-hairpin motif, ligand binding site, support vector machine, minimum redundancy maximum relevance.
以酶序列为基础,利用氨基酸组成、功率谱密度低频、预测二级结构、自相关函数值和基序频率等复合载体表达酶序列信息,提出了一种支持向量机(SVM)预测氧化还原酶18个亚类和酶解酶6个亚类的方法。通过Jackknife测试,总体成功率为89。关键词:酶,Â-hairpin基序,配体结合位点,支持向量机,最小冗余,最大相关性。
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引用次数: 4
期刊
2011 10th IEEE/ACIS International Conference on Computer and Information Science
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